System

Abstract

The system aims to automatically generate a logical configuration diagram of a network.SOLUTION: A system according to an embodiment includes a network data collection unit and a logical configuration diagram generation unit. A network data collection part automatically collects each device in a network and connection information. The logical configuration diagram generation unit generates a logical configuration diagram based on the network data collected by the network data collection unit. The network data collection unit automatically updates the logical configuration diagram when a network device is added or changed. The logical configuration diagram generation unit analyzes a traffic pattern of the network, proposes an optimum topology, specifies an influence range of a network failure in real time, and displays the influence range on the logical configuration diagram.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional technology, the logical configuration diagram of the network has to be created manually, which has resulted in inefficient maintenance and operation and high costs.

[0005] The system according to the embodiment aims to automatically generate a logical configuration diagram of a network. [Means for solving the problem]

[0006] A system according to an embodiment includes a network data collection unit and a logical configuration diagram generation unit. The network data collection unit automatically collects information about devices and connections within a network. The logical configuration diagram generation unit generates a logical configuration diagram based on the network data collected by the network data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a logical configuration diagram of a network. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A network management system according to an embodiment of the present invention is a system that automatically generates a logical configuration diagram of a network using a generation AI. This system automatically collects information on each device and connection within the network and generates a logical configuration diagram based on the collected network data. This allows the network management system to reduce the risk of prolonged system failures and reduce maintenance and operation costs.

[0029] A network management system according to an embodiment includes a network data collection unit and a logical configuration diagram generation unit. The network data collection unit automatically collects information about devices and connections within a network. For example, the network data collection unit collects IP addresses, MAC addresses, and connection port information from devices such as routers, switches, and servers. The network data collection unit can also collect device operation status and performance data using network scanning tools and log data. For example, the network data collection unit collects data such as CPU usage, memory usage, and network traffic volume from network devices. The network data collection unit can also analyze security logs of network devices to detect potential threats and abnormal behavior. For example, the network data collection unit identifies unauthorized access and abnormal traffic patterns. The logical configuration diagram generation unit generates a logical configuration diagram based on the network data collected by the network data collection unit. For example, the logical configuration diagram generation unit analyzes the connection relationships between routers and switches and plots them as a diagram. In addition, the logical configuration diagram generation unit can identify the location of a network failure and display the extent of its impact on the logical configuration diagram. For example, the logical configuration diagram generation unit displays specific step-by-step procedures for dealing with a failure location. Furthermore, the logical configuration diagram generation unit can automatically update the logical configuration diagram when a network device is added or changed. For example, the logical configuration diagram generation unit updates the configuration diagram when a new device is connected to the network. This allows the network management system according to the embodiment to reduce the risk of prolonged system failures and reduce maintenance and operation costs. For example, it enables a quick response when a network failure occurs, improving the work efficiency of the maintenance department. Furthermore, by always maintaining the latest network configuration information, stable system operation is possible.

[0030] The network data collection unit can collect IP addresses, MAC addresses, and connection port information from devices such as routers, switches, and servers. For example, the network data collection unit collects IP addresses, MAC addresses, and connection port information from devices such as routers, switches, and servers. For example, the network data collection unit collects IP addresses of routers to understand the connection relationships between devices within the network. The network data collection unit can also collect MAC addresses of switches to identify the physical connection relationships between devices. The network data collection unit can also collect connection port information of servers and analyze the flow of network traffic. By collecting detailed information about network devices, a more accurate logical configuration diagram can be generated.

[0031] The logical configuration diagram generation unit can analyze the connection relationships between routers and switches and draw them as a diagram. The logical configuration diagram generation unit can analyze, for example, the connection relationships between routers and switches and draw them as a diagram. For example, the logical configuration diagram generation unit can analyze the physical connection relationships between routers and switches and visually represent the network topology. The logical configuration diagram generation unit can also analyze the logical connection relationships between routers and switches and visually represent the data flow. The logical configuration diagram generation unit can also analyze the connection relationships between routers and switches and visually represent the network redundancy and load balancing status. This allows the network connection relationships to be visually understood.

[0032] The logical configuration diagram generator can identify the location of a network failure and display the extent of its impact on the logical configuration diagram. For example, when a network failure occurs, the logical configuration diagram generator can identify the location of the failure and display the extent of its impact on the logical configuration diagram. For example, the logical configuration diagram generator can highlight the device where the failure occurred and its connection destination to visually show the extent of the impact. The logical configuration diagram generator can also display specific step-by-step response procedures for the location of the failure. The logical configuration diagram generator can also predict the extent of the impact of the failure and propose countermeasures to be taken in advance. This allows the extent of the impact of a network failure to be quickly identified and supports response.

[0033] The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed. The network data collection unit automatically updates the logical configuration diagram when, for example, a network device is added or changed. For example, the network data collection unit updates the configuration diagram when a new device is connected to the network. The network data collection unit can also update the configuration diagram when the settings of an existing device are changed. The network data collection unit can also collect physical layout information of network devices and display an integrated physical configuration diagram and logical configuration diagram. This allows changes to network devices to be responded to immediately and the configuration diagram to be kept up to date.

[0034] The network data collection unit collects the operation status and performance data of network devices in real time, enabling the status of the network to be grasped. For example, the network data collection unit monitors the operation status of network devices in real time and collects performance data. For example, the network data collection unit collects data such as the CPU usage rate, memory usage rate, and network traffic volume of the network devices to grasp the status of the network. The network data collection unit can also analyze the security logs of the network devices to detect potential threats and abnormal behavior. The network data collection unit can also monitor the operation status and performance data of network devices in real time and issue an alert if an abnormality is detected. This allows the status of the network to be grasped in real time and enables rapid response.

[0035] The network data collection unit can analyze security logs of network devices to detect potential threats and abnormal behavior. The network data collection unit, for example, analyzes security logs of network devices in real time to detect potential threats. For example, the network data collection unit identifies unauthorized access and abnormal traffic patterns. The network data collection unit can also analyze security logs of network devices to detect abnormal behavior. The network data collection unit can also analyze security logs of network devices and issue an alert if it detects a potential threat or abnormal behavior. This enhances network security and enables early detection of potential threats.

[0036] The network data collection unit can collect physical layout information of network devices and display an integrated physical configuration diagram and a logical configuration diagram. The network data collection unit, for example, collects physical layout information of network devices and generates a physical configuration diagram. For example, the network data collection unit collects device installation locations and cable wiring information. The network data collection unit can also integrate and display the physical configuration diagram and the logical configuration diagram. The network data collection unit can also build a system that collects physical layout information of network devices and displays the integrated physical configuration diagram and the logical configuration diagram. In this way, by integrating the physical layout information and the logical configuration diagram, the overall picture of the network can be grasped more intuitively.

[0037] The network data collection unit can add data collection functions that support different network protocols. For example, the network data collection unit collects IPv6 address and MPLS label information. The network data collection unit can also collect BGP route information. The network data collection unit can also add data collection functions that support different network protocols to build a system that collects information about network devices. This makes it possible to support a wider range of network environments by supporting different network protocols.

[0038] The logical configuration diagram generation unit can analyze network traffic patterns and propose the optimal topology. The logical configuration diagram generation unit can, for example, use generation AI to analyze network traffic patterns and propose the optimal topology. For example, the logical configuration diagram generation unit can identify traffic concentration points and propose a topology for load balancing. The logical configuration diagram generation unit can also analyze traffic peak times and traffic types and propose the optimal topology. The logical configuration diagram generation unit can also analyze traffic direction and traffic importance and propose the optimal topology. In this way, network efficiency is improved by analyzing network traffic patterns and proposing the optimal topology.

[0039] The logical configuration diagram generator can display the operating status and performance data of each device on the logical configuration diagram in real time. The logical configuration diagram generator can, for example, use generation AI to display the operating status of each device on the logical configuration diagram in real time. For example, the logical configuration diagram generator can display the CPU usage rate and memory usage rate of a device on the diagram. The logical configuration diagram generator can also display the amount of network traffic and the operating status of a device in real time. The logical configuration diagram generator can also display device performance data in real time, allowing the status of the network to be grasped immediately. This allows the status of the network to be grasped immediately by displaying the operating status and performance data of each device in real time.

[0040] The logical configuration diagram generation unit can generate the logical configuration diagram as a 3D model, allowing the network configuration to be intuitively understood using VR or AR. The logical configuration diagram generation unit can, for example, use a generation AI to generate the logical configuration diagram as a 3D model, allowing the network configuration to be intuitively understood using VR. For example, the logical configuration diagram generation unit can use a VR headset to check the network configuration in a virtual space. The logical configuration diagram generation unit can also use an AR application to display the network configuration superimposed on real space. The logical configuration diagram generation unit can also use the 3D model to visually display the arrangement and connection relationships of network devices. In this way, using the 3D model makes it easier to intuitively understand the network configuration.

[0041] The logical configuration diagram generator visually displays the interrelationships between different network segments, making troubleshooting easier. The logical configuration diagram generator visually displays the interrelationships between different network segments, for example, using generation AI. For example, the logical configuration diagram generator displays the traffic flow between segments on the diagram. The logical configuration diagram generator can also visually display the connection relationships and dependencies between segments. The logical configuration diagram generator can also analyze the data flow between segments to assist in troubleshooting. This makes troubleshooting easier by visually displaying the interrelationships between different network segments.

[0042] The logical configuration diagram generator can identify the extent of the impact of a network failure in real time and display it on the logical configuration diagram. For example, the logical configuration diagram generator can use generation AI to identify the extent of the impact of a network failure in real time and display it on the logical configuration diagram. For example, the logical configuration diagram generator can highlight the device where the failure occurred and its connection destination to visually show the extent of the impact. The logical configuration diagram generator can also display specific step-by-step response procedures for the location of the failure. The logical configuration diagram generator can also predict the extent of the impact of the failure and make suggestions for taking countermeasures in advance. This allows the extent of the impact of a network failure to be identified in real time and supports rapid response.

[0043] The logical configuration diagram generator can automatically generate response procedures when a failure occurs and display them on the logical configuration diagram. The logical configuration diagram generator can, for example, use generation AI to automatically generate response procedures when a failure occurs and display them on the logical configuration diagram. For example, the logical configuration diagram generator can display specific response procedures for the failure location step by step. The logical configuration diagram generator can also generate response procedures based on past response history and best practices. The logical configuration diagram generator can also automatically generate response procedures when a failure occurs and display them to administrators in real time. This allows for the automatic generation of response procedures when a failure occurs and supports rapid response.

[0044] The logical configuration diagram generation unit can predict the scope of impact when a failure occurs and make suggestions for taking countermeasures in advance. The logical configuration diagram generation unit, for example, uses generation AI to predict the scope of impact when a failure occurs and makes suggestions for taking countermeasures in advance. For example, the logical configuration diagram generation unit predicts the scope of impact when a specific device fails and proposes an alternative route. The logical configuration diagram generation unit can also predict the scope of impact when a failure occurs and make suggestions for taking countermeasures in advance. The logical configuration diagram generation unit can also predict the scope of impact when a failure occurs and propose specific countermeasures to the administrator. In this way, the impact of a failure can be minimized by predicting the scope of impact when a failure occurs and taking countermeasures in advance.

[0045] The network data collection unit can periodically collect network data and update the logical configuration diagram. The network data collection unit, for example, uses a generation AI to periodically collect network data and automatically update the logical configuration diagram. For example, the network data collection unit collects network device information at a fixed time every day and generates the latest configuration diagram. The network data collection unit can also collect network data and update the configuration diagram based on a regular weekly or monthly schedule. The network data collection unit can also automatically update the configuration diagram when a network device is added or changed. In this way, by periodically collecting network data and maintaining the latest logical configuration diagram, stable network operation is supported.

[0046] The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed. The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed, for example, using generative AI. For example, the network data collection unit updates the configuration diagram when a new device is connected to the network. The network data collection unit can also update the configuration diagram when the settings of an existing device are changed. The network data collection unit can also collect physical placement information of network devices and display an integrated physical configuration diagram and logical configuration diagram. This allows for immediate response to changes in network devices and keeps the configuration diagram up to date.

[0047] The network data collection unit can monitor the operating status and performance data of network devices in real time and issue an alert if an abnormality is detected. The network data collection unit can, for example, use generative AI to monitor the operating status and performance data of network devices in real time and issue an alert if an abnormality is detected. For example, the network data collection unit can issue an alert if CPU usage is abnormally high. The network data collection unit can also issue an alert if memory usage is abnormally high. The network data collection unit can also issue an alert if network traffic volume increases abnormally. This enables network device abnormalities to be detected in real time and supports rapid response.

[0048] The network data collection unit can reduce the time and effort required to manually create logical configuration diagrams. For example, the network data collection unit reduces the time and effort required to manually create logical configuration diagrams by using generation AI. For example, creating a logical configuration diagram that previously took several hours can now be completed in just a few minutes by using generation AI. The network data collection unit can also simplify work procedures and reduce time and effort by automating manual tasks. The network data collection unit can also significantly reduce manual work by automatically collecting information on network devices and automatically generating logical configuration diagrams. This reduces the time and effort required to manually create logical configuration diagrams and supports efficient maintenance operations.

[0049] The network data collection unit can monitor the operating status and performance data of network devices in real time and issue an alert if an abnormality is detected. The network data collection unit can, for example, use generative AI to monitor the operating status and performance data of network devices in real time and issue an alert if an abnormality is detected. For example, the network data collection unit can issue an alert if CPU usage is abnormally high. The network data collection unit can also issue an alert if memory usage is abnormally high. The network data collection unit can also issue an alert if network traffic volume increases abnormally. This enables network device abnormalities to be detected in real time and supports rapid response.

[0050] The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed. The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed, for example, using generative AI. For example, the network data collection unit updates the configuration diagram when a new device is connected to the network. The network data collection unit can also update the configuration diagram when the settings of an existing device are changed. The network data collection unit can also collect physical placement information of network devices and display an integrated physical configuration diagram and logical configuration diagram. This allows for immediate response to changes in network devices and keeps the configuration diagram up to date.

[0051] The network data collection unit can reduce the time and effort required to manually create logical configuration diagrams. For example, the network data collection unit reduces the time and effort required to manually create logical configuration diagrams by using generation AI. For example, creating a logical configuration diagram that previously took several hours can now be completed in just a few minutes by using generation AI. The network data collection unit can also simplify work procedures and reduce time and effort by automating manual tasks. The network data collection unit can also significantly reduce manual work by automatically collecting information on network devices and automatically generating logical configuration diagrams. This reduces the time and effort required to manually create logical configuration diagrams and supports efficient maintenance operations.

[0052] The network data collection unit can monitor the operating status and performance data of network devices in real time and issue an alert if an abnormality is detected. The network data collection unit can, for example, use generative AI to monitor the operating status and performance data of network devices in real time and issue an alert if an abnormality is detected. For example, the network data collection unit can issue an alert if CPU usage is abnormally high. The network data collection unit can also issue an alert if memory usage is abnormally high. The network data collection unit can also issue an alert if network traffic volume increases abnormally. This enables network device abnormalities to be detected in real time and supports rapid response.

[0053] The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed. The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed, for example, using generative AI. For example, the network data collection unit updates the configuration diagram when a new device is connected to the network. The network data collection unit can also update the configuration diagram when the settings of an existing device are changed. The network data collection unit can also collect physical placement information of network devices and display an integrated physical configuration diagram and logical configuration diagram. This allows for immediate response to changes in network devices and keeps the configuration diagram up to date.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The network management system may further include an energy consumption monitoring unit. The energy consumption monitoring unit monitors the energy consumption of each network device in real time and helps optimize energy efficiency. For example, the energy consumption monitoring unit may measure the power consumption of routers and switches and identify devices with low energy efficiency. The energy consumption monitoring unit may also analyze energy consumption patterns across the entire network and make suggestions for improving energy efficiency. Furthermore, the energy consumption monitoring unit may collect historical energy consumption data and help improve energy efficiency over the long term. This allows the network management system to reduce operational costs and mitigate environmental impact through energy consumption optimization.

[0056] The network management system may further include a user feedback collection unit. The user feedback collection unit collects feedback from network administrators and end users to help improve the system. For example, the user feedback collection unit provides an interface through which network administrators can input opinions regarding the usability and functionality of the system. The user feedback collection unit may also collect feedback regarding network performance from end users and identify areas for improvement in the network. Furthermore, the user feedback collection unit may analyze the collected feedback and reflect it in system updates and the development of new functions. This enables the network management system to flexibly respond to user needs and improve system satisfaction.

[0057] The network management system can further include a predictive analysis unit. The predictive analysis unit predicts future network traffic and failures based on collected network data. For example, the predictive analysis unit analyzes past traffic data to predict peak traffic times and trends in traffic volume. The predictive analysis unit can also predict the risk of failures in specific devices or segments based on past failure data. Furthermore, the predictive analysis unit can propose network optimization and preventive measures based on the prediction results. This allows the network management system to prepare for future traffic and failures and support stable network operation.

[0058] The network management system may further include an automatic restoration unit. The automatic restoration unit automatically executes restoration procedures when a network failure occurs, minimizing the impact of the failure. For example, the automatic restoration unit automatically sets up an alternative route when a specific device fails. The automatic restoration unit can also automatically adjust the load balancing of network traffic to prevent traffic congestion. Furthermore, the automatic restoration unit can automatically activate a backup device when a failure occurs, ensuring continuity of service. This allows the network management system to support rapid response when a failure occurs and ensure stable network operation.

[0059] The network management system may further include an education support unit, which provides network administrators and end users with information on how to use the system and basic network knowledge. For example, the education support unit may provide online tutorials and video guides to help users learn how to operate the system. The education support unit may also provide documents that explain basic network concepts and troubleshooting methods. Furthermore, the education support unit may hold regular webinars and training sessions to help network administrators improve their skills. In this way, the network management system can improve users' knowledge and skills and promote effective use of the system.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The network data collection unit automatically collects information about each device and connection within the network. For example, it collects IP addresses, MAC addresses, and connection port information from devices such as routers, switches, and servers. It also uses network scanning tools and log data to collect device operating status and performance data (CPU usage, memory usage, network traffic volume, etc.). It also analyzes security logs from network devices to detect potential threats and abnormal behavior (such as unauthorized access or abnormal traffic patterns). Step 2: The logical configuration diagram generator generates a logical configuration diagram based on the network data collected by the network data collector. For example, it analyzes the connection relationships between routers and switches and draws them as a diagram. It can also identify the location of a network failure when one occurs and display the extent of its impact on the logical configuration diagram. Furthermore, it automatically updates the logical configuration diagram when network devices are added or changed.

[0062] (Example 2) A network management system according to an embodiment of the present invention is a system that automatically generates a logical configuration diagram of a network using a generation AI. This system automatically collects information on each device and connection within the network and generates a logical configuration diagram based on the collected network data. This allows the network management system to reduce the risk of prolonged system failures and reduce maintenance and operation costs.

[0063] A network management system according to an embodiment includes a network data collection unit and a logical configuration diagram generation unit. The network data collection unit automatically collects information about devices and connections within a network. For example, the network data collection unit collects IP addresses, MAC addresses, and connection port information from devices such as routers, switches, and servers. The network data collection unit can also collect device operation status and performance data using network scanning tools and log data. For example, the network data collection unit collects data such as CPU usage, memory usage, and network traffic volume from network devices. The network data collection unit can also analyze security logs of network devices to detect potential threats and abnormal behavior. For example, the network data collection unit identifies unauthorized access and abnormal traffic patterns. The logical configuration diagram generation unit generates a logical configuration diagram based on the network data collected by the network data collection unit. For example, the logical configuration diagram generation unit analyzes the connection relationships between routers and switches and plots them as a diagram. In addition, the logical configuration diagram generation unit can identify the location of a network failure and display the extent of its impact on the logical configuration diagram. For example, the logical configuration diagram generation unit displays specific step-by-step procedures for dealing with a failure location. Furthermore, the logical configuration diagram generation unit can automatically update the logical configuration diagram when a network device is added or changed. For example, the logical configuration diagram generation unit updates the configuration diagram when a new device is connected to the network. This allows the network management system according to the embodiment to reduce the risk of prolonged system failures and reduce maintenance and operation costs. For example, it enables a quick response when a network failure occurs, improving the work efficiency of the maintenance department. Furthermore, by always maintaining the latest network configuration information, stable system operation is possible.

[0064] The network data collection unit can collect IP addresses, MAC addresses, and connection port information from devices such as routers, switches, and servers. For example, the network data collection unit collects IP addresses, MAC addresses, and connection port information from devices such as routers, switches, and servers. For example, the network data collection unit collects IP addresses of routers to understand the connection relationships between devices within the network. The network data collection unit can also collect MAC addresses of switches to identify the physical connection relationships between devices. The network data collection unit can also collect connection port information of servers and analyze the flow of network traffic. By collecting detailed information about network devices, a more accurate logical configuration diagram can be generated.

[0065] The logical configuration diagram generation unit can analyze the connection relationships between routers and switches and draw them as a diagram. The logical configuration diagram generation unit can analyze, for example, the connection relationships between routers and switches and draw them as a diagram. For example, the logical configuration diagram generation unit can analyze the physical connection relationships between routers and switches and visually represent the network topology. The logical configuration diagram generation unit can also analyze the logical connection relationships between routers and switches and visually represent the data flow. The logical configuration diagram generation unit can also analyze the connection relationships between routers and switches and visually represent the network redundancy and load balancing status. This allows the network connection relationships to be visually understood.

[0066] The logical configuration diagram generator can identify the location of a network failure and display the extent of its impact on the logical configuration diagram. For example, when a network failure occurs, the logical configuration diagram generator can identify the location of the failure and display the extent of its impact on the logical configuration diagram. For example, the logical configuration diagram generator can highlight the device where the failure occurred and its connection destination to visually show the extent of the impact. The logical configuration diagram generator can also display specific step-by-step response procedures for the location of the failure. The logical configuration diagram generator can also predict the extent of the impact of the failure and propose countermeasures to be taken in advance. This allows the extent of the impact of a network failure to be quickly identified and supports response.

[0067] The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed. The network data collection unit automatically updates the logical configuration diagram when, for example, a network device is added or changed. For example, the network data collection unit updates the configuration diagram when a new device is connected to the network. The network data collection unit can also update the configuration diagram when the settings of an existing device are changed. The network data collection unit can also collect physical layout information of network devices and display an integrated physical configuration diagram and logical configuration diagram. This allows changes to network devices to be responded to immediately and the configuration diagram to be kept up to date.

[0068] The network data collection unit collects the operation status and performance data of network devices in real time, enabling the status of the network to be grasped. For example, the network data collection unit monitors the operation status of network devices in real time and collects performance data. For example, the network data collection unit collects data such as the CPU usage rate, memory usage rate, and network traffic volume of the network devices to grasp the status of the network. The network data collection unit can also analyze the security logs of the network devices to detect potential threats and abnormal behavior. The network data collection unit can also monitor the operation status and performance data of network devices in real time and issue an alert if an abnormality is detected. This allows the status of the network to be grasped in real time and enables rapid response.

[0069] The network data collection unit can analyze security logs of network devices to detect potential threats and abnormal behavior. The network data collection unit, for example, analyzes security logs of network devices in real time to detect potential threats. For example, the network data collection unit identifies unauthorized access and abnormal traffic patterns. The network data collection unit can also analyze security logs of network devices to detect abnormal behavior. The network data collection unit can also analyze security logs of network devices and issue an alert if it detects a potential threat or abnormal behavior. This enhances network security and enables early detection of potential threats.

[0070] The network data collection unit can collect physical layout information of network devices and display an integrated physical configuration diagram and a logical configuration diagram. The network data collection unit, for example, collects physical layout information of network devices and generates a physical configuration diagram. For example, the network data collection unit collects device installation locations and cable wiring information. The network data collection unit can also integrate and display the physical configuration diagram and the logical configuration diagram. The network data collection unit can also build a system that collects physical layout information of network devices and displays the integrated physical configuration diagram and the logical configuration diagram. In this way, by integrating the physical layout information and the logical configuration diagram, the overall picture of the network can be grasped more intuitively.

[0071] The network data collection unit can add data collection functions that support different network protocols. For example, the network data collection unit collects IPv6 address and MPLS label information. The network data collection unit can also collect BGP route information. The network data collection unit can also add data collection functions that support different network protocols to build a system that collects information about network devices. This makes it possible to support a wider range of network environments by supporting different network protocols.

[0072] The network data collection unit can use the emotion estimation function to estimate the stress level of a network administrator and automatically issue an alert if the stress level is high. For example, the network data collection unit can use the emotion estimation function to estimate the stress level of a network administrator and automatically issue an alert if the stress level is high. For example, the network data collection unit can analyze the administrator's facial expressions and tone of voice to detect stress or impatience. The network data collection unit can also collect the administrator's biometric data (heart rate and electrodermal activity) using a sensor and analyze the stress level using an emotion estimation algorithm. The network data collection unit can also use the emotion estimation function to monitor the administrator's emotions when changing network device settings and make suggestions to prevent configuration errors. This reduces the burden on the administrator by monitoring the network administrator's stress level and issuing alerts at the appropriate time.

[0073] The logical configuration diagram generation unit can analyze network traffic patterns and propose the optimal topology. The logical configuration diagram generation unit can, for example, use generation AI to analyze network traffic patterns and propose the optimal topology. For example, the logical configuration diagram generation unit can identify traffic concentration points and propose a topology for load balancing. The logical configuration diagram generation unit can also analyze traffic peak times and traffic types and propose the optimal topology. The logical configuration diagram generation unit can also analyze traffic direction and traffic importance and propose the optimal topology. In this way, network efficiency is improved by analyzing network traffic patterns and proposing the optimal topology.

[0074] The logical configuration diagram generator can display the operating status and performance data of each device on the logical configuration diagram in real time. The logical configuration diagram generator can, for example, use generation AI to display the operating status of each device on the logical configuration diagram in real time. For example, the logical configuration diagram generator can display the CPU usage rate and memory usage rate of a device on the diagram. The logical configuration diagram generator can also display the amount of network traffic and the operating status of a device in real time. The logical configuration diagram generator can also display device performance data in real time, allowing the status of the network to be grasped immediately. This allows the status of the network to be grasped immediately by displaying the operating status and performance data of each device in real time.

[0075] The logical configuration diagram generation unit can use an emotion estimation function to automatically generate a configuration diagram that is visually easy to understand based on the emotions of the network administrator. The logical configuration diagram generation unit can, for example, use a generation AI to automatically generate a configuration diagram that is visually easy to understand based on the emotions of the network administrator. For example, the logical configuration diagram generation unit can generate a simple configuration diagram when the administrator is feeling stressed. The logical configuration diagram generation unit can also generate a detailed configuration diagram when the administrator is relaxed. The logical configuration diagram generation unit can also change the color and layout of the configuration diagram based on the administrator's emotions. In this way, by generating a configuration diagram that corresponds to the network administrator's emotions, the burden on the administrator is reduced and efficient management is supported.

[0076] The logical configuration diagram generation unit can generate the logical configuration diagram as a 3D model, allowing the network configuration to be intuitively understood using VR or AR. The logical configuration diagram generation unit can, for example, use a generation AI to generate the logical configuration diagram as a 3D model, allowing the network configuration to be intuitively understood using VR. For example, the logical configuration diagram generation unit can use a VR headset to check the network configuration in a virtual space. The logical configuration diagram generation unit can also use an AR application to display the network configuration superimposed on real space. The logical configuration diagram generation unit can also use the 3D model to visually display the arrangement and connection relationships of network devices. In this way, using the 3D model makes it easier to intuitively understand the network configuration.

[0077] The logical configuration diagram generator visually displays the interrelationships between different network segments, making troubleshooting easier. The logical configuration diagram generator visually displays the interrelationships between different network segments, for example, using generation AI. For example, the logical configuration diagram generator displays the traffic flow between segments on the diagram. The logical configuration diagram generator can also visually display the connection relationships and dependencies between segments. The logical configuration diagram generator can also analyze the data flow between segments to assist in troubleshooting. This makes troubleshooting easier by visually displaying the interrelationships between different network segments.

[0078] The logical configuration diagram generation unit uses an emotion estimation function to highlight the parts that the network administrator is most interested in, thereby supporting a quick response. The logical configuration diagram generation unit, for example, uses a generation AI to highlight the parts that the network administrator is most interested in based on their emotions. For example, the logical configuration diagram generation unit highlights parts that cause stress to the administrator, thereby supporting a quick response. The logical configuration diagram generation unit can also highlight parts that the administrator is interested in using colors or icons. The logical configuration diagram generation unit can also highlight important information based on the administrator's emotions, thereby supporting a quick response. This enables a quick response by highlighting the parts that the network administrator is most interested in.

[0079] The logical configuration diagram generator can identify the extent of the impact of a network failure in real time and display it on the logical configuration diagram. For example, the logical configuration diagram generator can use generation AI to identify the extent of the impact of a network failure in real time and display it on the logical configuration diagram. For example, the logical configuration diagram generator can highlight the device where the failure occurred and its connection destination to visually show the extent of the impact. The logical configuration diagram generator can also display specific step-by-step response procedures for the location of the failure. The logical configuration diagram generator can also predict the extent of the impact of the failure and make suggestions for taking countermeasures in advance. This allows the extent of the impact of a network failure to be identified in real time and supports rapid response.

[0080] The logical configuration diagram generator can automatically generate response procedures when a failure occurs and display them on the logical configuration diagram. The logical configuration diagram generator can, for example, use generation AI to automatically generate response procedures when a failure occurs and display them on the logical configuration diagram. For example, the logical configuration diagram generator can display specific response procedures for the failure location step by step. The logical configuration diagram generator can also generate response procedures based on past response history and best practices. The logical configuration diagram generator can also automatically generate response procedures when a failure occurs and display them to administrators in real time. This allows for the automatic generation of response procedures when a failure occurs and supports rapid response.

[0081] The logical configuration diagram generation unit can predict the scope of impact when a failure occurs and make suggestions for taking countermeasures in advance. The logical configuration diagram generation unit, for example, uses generation AI to predict the scope of impact when a failure occurs and makes suggestions for taking countermeasures in advance. For example, the logical configuration diagram generation unit predicts the scope of impact when a specific device fails and proposes an alternative route. The logical configuration diagram generation unit can also predict the scope of impact when a failure occurs and make suggestions for taking countermeasures in advance. The logical configuration diagram generation unit can also predict the scope of impact when a failure occurs and propose specific countermeasures to the administrator. In this way, the impact of a failure can be minimized by predicting the scope of impact when a failure occurs and taking countermeasures in advance.

[0082] The network data collection unit can periodically collect network data and update the logical configuration diagram. The network data collection unit, for example, uses a generation AI to periodically collect network data and automatically update the logical configuration diagram. For example, the network data collection unit collects network device information at a fixed time every day and generates the latest configuration diagram. The network data collection unit can also collect network data and update the configuration diagram based on a regular weekly or monthly schedule. The network data collection unit can also automatically update the configuration diagram when a network device is added or changed. In this way, by periodically collecting network data and maintaining the latest logical configuration diagram, stable network operation is supported.

[0083] The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed. The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed, for example, using generative AI. For example, the network data collection unit updates the configuration diagram when a new device is connected to the network. The network data collection unit can also update the configuration diagram when the settings of an existing device are changed. The network data collection unit can also collect physical placement information of network devices and display an integrated physical configuration diagram and logical configuration diagram. This allows for immediate response to changes in network devices and keeps the configuration diagram up to date.

[0084] The network data collection unit can monitor the operating status and performance data of network devices in real time and issue an alert if an abnormality is detected. The network data collection unit can, for example, use generative AI to monitor the operating status and performance data of network devices in real time and issue an alert if an abnormality is detected. For example, the network data collection unit can issue an alert if CPU usage is abnormally high. The network data collection unit can also issue an alert if memory usage is abnormally high. The network data collection unit can also issue an alert if network traffic volume increases abnormally. This enables network device abnormalities to be detected in real time and supports rapid response.

[0085] The network data collection unit can reduce the time and effort required to manually create logical configuration diagrams. For example, the network data collection unit reduces the time and effort required to manually create logical configuration diagrams by using generation AI. For example, creating a logical configuration diagram that previously took several hours can now be completed in just a few minutes by using generation AI. The network data collection unit can also simplify work procedures and reduce time and effort by automating manual tasks. The network data collection unit can also significantly reduce manual work by automatically collecting information on network devices and automatically generating logical configuration diagrams. This reduces the time and effort required to manually create logical configuration diagrams and supports efficient maintenance operations.

[0086] The network data collection unit can monitor the operating status and performance data of network devices in real time and issue an alert if an abnormality is detected. The network data collection unit can, for example, use generative AI to monitor the operating status and performance data of network devices in real time and issue an alert if an abnormality is detected. For example, the network data collection unit can issue an alert if CPU usage is abnormally high. The network data collection unit can also issue an alert if memory usage is abnormally high. The network data collection unit can also issue an alert if network traffic volume increases abnormally. This enables network device abnormalities to be detected in real time and supports rapid response.

[0087] The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed. The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed, for example, using generative AI. For example, the network data collection unit updates the configuration diagram when a new device is connected to the network. The network data collection unit can also update the configuration diagram when the settings of an existing device are changed. The network data collection unit can also collect physical placement information of network devices and display an integrated physical configuration diagram and logical configuration diagram. This allows for immediate response to changes in network devices and keeps the configuration diagram up to date.

[0088] The network data collection unit can reduce the time and effort required to manually create logical configuration diagrams. For example, the network data collection unit reduces the time and effort required to manually create logical configuration diagrams by using generation AI. For example, creating a logical configuration diagram that previously took several hours can now be completed in just a few minutes by using generation AI. The network data collection unit can also simplify work procedures and reduce time and effort by automating manual tasks. The network data collection unit can also significantly reduce manual work by automatically collecting information on network devices and automatically generating logical configuration diagrams. This reduces the time and effort required to manually create logical configuration diagrams and supports efficient maintenance operations.

[0089] The network data collection unit can monitor the operating status and performance data of network devices in real time and issue an alert if an abnormality is detected. The network data collection unit can, for example, use generative AI to monitor the operating status and performance data of network devices in real time and issue an alert if an abnormality is detected. For example, the network data collection unit can issue an alert if CPU usage is abnormally high. The network data collection unit can also issue an alert if memory usage is abnormally high. The network data collection unit can also issue an alert if network traffic volume increases abnormally. This enables network device abnormalities to be detected in real time and supports rapid response.

[0090] The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed. The network data collection unit can automatically update the logical configuration diagram when a network device is added or changed, for example, using generative AI. For example, the network data collection unit updates the configuration diagram when a new device is connected to the network. The network data collection unit can also update the configuration diagram when the settings of an existing device are changed. The network data collection unit can also collect physical placement information of network devices and display an integrated physical configuration diagram and logical configuration diagram. This allows for immediate response to changes in network devices and keeps the configuration diagram up to date.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The network management system may further include an energy consumption monitoring unit. The energy consumption monitoring unit monitors the energy consumption of each network device in real time and helps optimize energy efficiency. For example, the energy consumption monitoring unit may measure the power consumption of routers and switches and identify devices with low energy efficiency. The energy consumption monitoring unit may also analyze energy consumption patterns across the entire network and make suggestions for improving energy efficiency. Furthermore, the energy consumption monitoring unit may collect historical energy consumption data and help improve energy efficiency over the long term. This allows the network management system to reduce operational costs and mitigate environmental impact through energy consumption optimization.

[0093] The network management system may further include a user feedback collection unit. The user feedback collection unit collects feedback from network administrators and end users to help improve the system. For example, the user feedback collection unit provides an interface through which network administrators can input opinions regarding the usability and functionality of the system. The user feedback collection unit may also collect feedback regarding network performance from end users and identify areas for improvement in the network. Furthermore, the user feedback collection unit may analyze the collected feedback and reflect it in system updates and the development of new functions. This enables the network management system to flexibly respond to user needs and improve system satisfaction.

[0094] The network management system can further include a predictive analysis unit. The predictive analysis unit predicts future network traffic and failures based on collected network data. For example, the predictive analysis unit analyzes past traffic data to predict peak traffic times and trends in traffic volume. The predictive analysis unit can also predict the risk of failures in specific devices or segments based on past failure data. Furthermore, the predictive analysis unit can propose network optimization and preventive measures based on the prediction results. This allows the network management system to prepare for future traffic and failures and support stable network operation.

[0095] The network management system may further include an automatic restoration unit. The automatic restoration unit automatically executes restoration procedures when a network failure occurs, minimizing the impact of the failure. For example, the automatic restoration unit automatically sets up an alternative route when a specific device fails. The automatic restoration unit can also automatically adjust the load balancing of network traffic to prevent traffic congestion. Furthermore, the automatic restoration unit can automatically activate a backup device when a failure occurs, ensuring continuity of service. This allows the network management system to support rapid response when a failure occurs and ensure stable network operation.

[0096] The network management system may further include an education support unit, which provides network administrators and end users with information on how to use the system and basic network knowledge. For example, the education support unit may provide online tutorials and video guides to help users learn how to operate the system. The education support unit may also provide documents that explain basic network concepts and troubleshooting methods. Furthermore, the education support unit may hold regular webinars and training sessions to help network administrators improve their skills. In this way, the network management system can improve users' knowledge and skills and promote effective use of the system.

[0097] The network management system can also use emotion estimation to monitor the stress level of network administrators and suggest breaks at appropriate times. For example, the emotion estimation function can analyze the administrator's facial expressions and voice tone to detect stress and fatigue. The emotion estimation function can also collect the administrator's biometric data (heart rate and electrodermal activity) using sensors to analyze the stress level. Furthermore, the emotion estimation function can issue an alert to advise the administrator to take a break if the administrator is experiencing high stress. In this way, the network management system can support the administrator's health and reduce fatigue caused by working for long periods of time.

[0098] The network management system can further use the emotion estimation function to customize the interface based on the emotions of the network administrator. For example, the emotion estimation function can provide a simple and intuitive interface when the administrator is stressed. Alternatively, the emotion estimation function can provide an interface that displays detailed information when the administrator is relaxed. Furthermore, the emotion estimation function can change the color and layout of the interface according to the administrator's emotions to reduce visual burden. This allows the network management system to provide a flexible interface that responds to the administrator's emotions and improve work efficiency.

[0099] The network management system can further use the emotion estimation function to adjust the priority of alerts based on the emotions of the network administrator. For example, if the administrator is feeling highly stressed, the emotion estimation function can temporarily suppress low-priority alerts. The emotion estimation function can also display all alerts normally if the administrator is relaxed. Furthermore, the emotion estimation function can change the way alerts are displayed depending on the administrator's emotions to reduce visual burden. This allows the network management system to manage alerts according to the administrator's emotions and improve work efficiency.

[0100] The network management system can further use the emotion estimation function to provide troubleshooting support based on the emotions of the network administrator. For example, the emotion estimation function can provide a concise and easy-to-understand troubleshooting guide when the administrator is stressed. The emotion estimation function can also provide detailed troubleshooting procedures when the administrator is relaxed. Furthermore, the emotion estimation function can display troubleshooting procedures step by step according to the administrator's emotions, reducing visual burden. This allows the network management system to provide troubleshooting support according to the administrator's emotions and support rapid problem resolution.

[0101] The network management system can further use the emotion estimation function to provide a training program based on the emotions of the network administrator. For example, if the administrator is feeling stressed, the emotion estimation function can suggest a training program that will help the administrator relax. Also, if the administrator is relaxed, the emotion estimation function can provide a detailed training program for improving skills. Furthermore, the emotion estimation function can adjust the content and progress speed of the training program according to the administrator's emotions to support effective learning. In this way, the network management system can provide a training program that suits the administrator's emotions and support skill improvement.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The network data collection unit automatically collects information about each device and connection within the network. For example, it collects IP addresses, MAC addresses, and connection port information from devices such as routers, switches, and servers. It also uses network scanning tools and log data to collect device operating status and performance data (CPU usage, memory usage, network traffic volume, etc.). It also analyzes security logs from network devices to detect potential threats and abnormal behavior (such as unauthorized access or abnormal traffic patterns). Step 2: The logical configuration diagram generator generates a logical configuration diagram based on the network data collected by the network data collector. For example, it analyzes the connection relationships between routers and switches and draws them as a diagram. It can also identify the location of a network failure when one occurs and display the extent of its impact on the logical configuration diagram. Furthermore, it automatically updates the logical configuration diagram when network devices are added or changed.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a network data collection unit that automatically collects information about each device and connection in the network; a logical configuration diagram generating unit that generates a logical configuration diagram based on the network data collected by the network data collecting unit. A system characterized by:

2. The network data collection unit Automatically update the logical configuration diagram when network devices are added or changed 2. The system of claim 1.

3. The logical configuration diagram generation unit Analyze the traffic patterns of the network and propose the optimal topology 2. The system of claim 1.

4. The logical configuration diagram generation unit Identify the extent of the impact of a network failure in real time and display it on the logical configuration diagram 2. The system of claim 1.

5. The network data collection unit Estimates the stress level of network administrators and automatically issues alerts when stress levels are high 2. The system of claim 1.

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