system

Abstract

The system according to the embodiment aims to automatically generate a logical configuration diagram of a network and improve the efficiency of maintenance and operation. [Solution] A system according to an embodiment includes a data collection unit, a generation unit, and a display unit. The data collection unit acquires information from each device in a network. The generation unit analyzes the information collected by the data collection unit and generates a logical configuration diagram of the network. The display unit displays the logical configuration diagram generated by the generation unit.

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, logical configuration diagrams of networks are created manually, which can lead to prolonged system failures and increased maintenance and operation costs.

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

[0006] A system according to an embodiment includes a data collection unit, a generation unit, and a display unit. The data collection unit acquires information from each device in a network. The generation unit analyzes the information collected by the data collection unit and generates a logical configuration diagram of the network. The display unit displays the logical configuration diagram generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a logical configuration diagram of a network, thereby improving the efficiency of maintenance and operation. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) An automatic network configuration diagram generation system according to an embodiment of the present invention acquires information from each device in a network and automatically generates and displays a logical configuration diagram of the network using a generation AI. This system includes a data collection unit that acquires information from each device in the network, a generation unit that analyzes the collected information and generates a logical configuration diagram of the network, and a display unit that displays the generated logical configuration diagram. For example, the data collection unit acquires information from each device using SNMP or an API. The generation unit then analyzes the collected data using a machine learning algorithm to generate a logical configuration diagram of the network. Furthermore, the display unit visually displays the generated logical configuration diagram and provides zoom-in / zoom-out and filtering functions. This reduces the risk of prolonged system failures and reduces maintenance and operation costs. Furthermore, the system's innovability can be enhanced by adding a storage unit that stores the logical configuration diagrams generated by the generation unit and a comparison unit that compares them with previous configuration diagrams. This automatic network configuration diagram generation system reduces the risk of prolonged system failures and reduces maintenance and operation costs.

[0029] A network configuration diagram automatic generation system according to an embodiment includes a data collection unit, a generation unit, and a display unit. The data collection unit acquires information from each device in a network. The data collection unit acquires information from each device using, for example, Simple Network Management Protocol (SNMP) or an Application Programming Interface (API). For example, the data collection unit can acquire device status information and network traffic information using SNMP. The data collection unit can also acquire device configuration information and log information using an API. The generation unit analyzes the collected data using a machine learning algorithm to generate a logical configuration diagram of the network. The generation unit can analyze the data using a machine learning algorithm such as a neural network or a decision tree to automatically extract the network topology and connection relationships. The generation unit generates the logical configuration diagram of the network based on the collected data. The display unit visually displays the generated logical configuration diagram. The display unit can display the logical configuration diagram using, for example, a graph or chart. The display unit also provides zoom-in / zoom-out functions and filtering functions. For example, the display unit can zoom in and out of the logical configuration diagram using a mouse wheel or slider. The display unit can also provide a filtering function that displays only specific devices. As a result, the automatic network configuration diagram generation system according to the embodiment can automatically generate a logical configuration diagram of a network, reduce the risk of prolonged system failures, and reduce maintenance and operation costs.

[0030] The data collection unit can acquire information from each device using SNMP or an API. For example, the data collection unit can acquire device status information and network traffic information using versions such as SNMPv2 and SNMPv3. The data collection unit can also acquire device configuration information and log information using types such as REST API and SOAP API. For example, the data collection unit can acquire a device's CPU usage rate and memory usage rate using SNMPv2. The data collection unit can also acquire device configuration information using a REST API. Furthermore, the data collection unit can acquire device log information using a SOAP API. This allows the data collection unit to efficiently acquire information from each device by using SNMP or an API.

[0031] The generation unit can analyze the collected data using a machine learning algorithm and generate a logical configuration diagram. The generation unit can analyze the data using a machine learning algorithm, such as a neural network or a decision tree, and automatically extract the network topology and connection relationships. For example, the generation unit can analyze the connection relationships between devices using a neural network and generate a logical configuration diagram of the network. The generation unit can also analyze device status information using a decision tree and generate a network topology. Furthermore, the generation unit can group devices using a clustering algorithm and generate a logical configuration diagram of the network. For example, the generation unit can group devices using K-means clustering and generate a logical configuration diagram of the network. In this way, the generation unit can efficiently analyze the collected data and generate an accurate logical configuration diagram by using a machine learning algorithm.

[0032] The display unit visually displays the generated logical configuration diagram and can provide zoom-in / zoom-out and filtering functions. The display unit can display the logical configuration diagram using, for example, a graph or chart. For example, the display unit can display a graph of the network topology to visually display the connection relationships between devices. The display unit can also display a chart of network traffic information to visually display traffic fluctuations. The display unit also provides zoom-in / zoom-out functions. For example, the display unit can zoom in and out of the logical configuration diagram using a mouse wheel or a slider. The display unit also provides filtering functions. For example, the display unit can provide a filtering function to display only specific devices. This allows the display unit to visually display the generated logical configuration diagram and make it easier for the user to intuitively understand the logical configuration diagram.

[0033] The generation unit may include a storage unit that stores the generated logical configuration diagram. The storage unit may store the generated logical configuration diagram in a database, for example. For example, the storage unit may store the generated logical configuration diagram using an SQL database. The storage unit may also store the generated logical configuration diagram in a file. For example, the storage unit may store the generated logical configuration diagram in an XML file or a JSON file. The storage unit may also store the generated logical configuration diagram in cloud storage. For example, the storage unit may store the generated logical configuration diagram using a cloud storage service. In this way, the generation unit can store the generated logical configuration diagram for later reference.

[0034] The generation unit may include a comparison unit that compares the current configuration diagram with a past configuration diagram. The comparison unit can, for example, calculate the difference from past data and grasp changes in the network. For example, the comparison unit can compare the current configuration diagram with the past configuration diagram to detect addition or deletion of devices, changes in connection relationships, etc. The comparison unit can also compare specific parameters. For example, the comparison unit can compare device status information and network traffic information to detect abnormal fluctuations. Furthermore, the comparison unit can visually compare the current configuration diagram with the past configuration diagram. For example, the comparison unit can overlay the current configuration diagram with the past configuration diagram to visually grasp changes. This allows the generation unit to more easily grasp changes in the network by comparing with the past configuration diagram.

[0035] The data collection unit can monitor network traffic conditions in real time and acquire device information at appropriate times. The data collection unit can monitor network traffic conditions in real time using, for example, packet capture or traffic monitoring tools. For example, the data collection unit can collect packets on the network using packet capture and monitor traffic fluctuations. The data collection unit can also monitor network traffic conditions in real time using a traffic monitoring tool. Furthermore, the data collection unit can adjust the timing of acquiring device information according to network traffic conditions. For example, the data collection unit can intensively acquire device information during times of low traffic. Furthermore, the data collection unit can acquire only important device information during times of high traffic. Furthermore, the data collection unit can predict traffic fluctuations and acquire device information at optimal timing. As a result, the data collection unit can acquire device information according to network traffic conditions, thereby efficiently collecting information.

[0036] The data collection unit can determine the priority of information to be acquired based on the device's operating status or error information. The data collection unit can monitor the operating status of the device, such as the CPU usage rate and memory usage rate, and prioritize acquiring information from operating devices. The data collection unit can also monitor error information such as log files and error messages and acquire detailed information from devices in which errors are occurring. The data collection unit can also periodically acquire information from devices with unstable operating status. For example, the data collection unit can prioritize acquiring information from devices with high CPU usage rates and monitor network load. The data collection unit can also acquire detailed log information from devices in which error messages are frequently occurring and identify the cause of the problem. In this way, the data collection unit can prioritize acquiring important information by determining the priority of information based on the device's operating status and error information.

[0037] When acquiring device information, the data collection unit can take the geographical distribution of the network into consideration when collecting information. For example, the data collection unit can prioritize acquiring information from geographically close devices by taking the geographical distribution of the network into consideration. The data collection unit can also periodically acquire information from geographically distant devices. Furthermore, the data collection unit can adjust the frequency of information acquisition based on the geographical distribution. For example, the data collection unit can frequently acquire information from devices in the same region to optimize network traffic between regions. The data collection unit can also periodically acquire information from devices in different regions to grasp the overall network status. In this way, the data collection unit can efficiently collect information by collecting information based on the geographical distribution of the network.

[0038] When acquiring device information, the data collection unit can cooperate with other network management systems to realize centralized information management. The data collection unit can, for example, integrate and collect information from other network management systems. For example, the data collection unit can cooperate with other network management systems, such as an SNMP manager or a network monitoring tool, to avoid information duplication. The data collection unit can also determine the priority of information to be acquired by referring to information from other network management systems. Furthermore, the data collection unit can realize centralized information management by coordinating with other network management systems. For example, the data collection unit can integrate information from other network management systems and centrally grasp the status of the entire network. As a result, the data collection unit can cooperate with other network management systems to realize centralized information management, thereby enabling efficient information collection.

[0039] The generation unit can evaluate the reliability of the collected data and generate a logical configuration diagram by preferentially using highly reliable data. The generation unit can evaluate the reliability of the data based on, for example, the origin and consistency of the data. For example, the generation unit can generate a detailed logical configuration diagram by preferentially using data from highly reliable devices. The generation unit can also evaluate the reliability of the data and exclude low-reliability data. Furthermore, the generation unit can accurately extract a network topology and connection relationships based on the highly reliable data. For example, the generation unit can evaluate the consistency of the data and preferentially use data with high consistency. The generation unit can also evaluate the origin of the data and preferentially use data from highly reliable devices. In this way, the generation unit can generate an accurate logical configuration diagram by evaluating the reliability of the collected data and preferentially using the highly reliable data to generate the logical configuration diagram.

[0040] The generation unit can generate an optimal logical configuration diagram by taking into account the network topology and the interrelationships of devices. The generation unit can generate an optimal logical configuration diagram based on, for example, the network topology. For example, the generation unit can generate a logical configuration diagram of a network by taking into account topologies such as a star type or a mesh type. The generation unit can also generate a detailed logical configuration diagram by taking into account the interrelationships of devices. For example, the generation unit can generate a logical configuration diagram of a network by taking into account the connection relationships and dependencies between devices. Furthermore, the generation unit can generate an optimal logical configuration diagram by integrating the network topology and the interrelationships of devices. For example, the generation unit can generate a logical configuration diagram of the entire network based on the network topology and the interrelationships of devices. In this way, the generation unit can generate an optimal logical configuration diagram by taking into account the network topology and the interrelationships of devices.

[0041] The generation unit can generate the logical configuration diagram while taking into consideration the network security policy. The generation unit can generate the logical configuration diagram, for example, based on the network security policy. For example, the generation unit can generate the logical configuration diagram while taking into consideration security policies such as access control and data encryption. The generation unit can also generate a detailed logical configuration diagram while taking into consideration the security policy. For example, the generation unit can generate an optimal logical configuration diagram while taking into consideration the network topology and connection relationships based on the security policy. Furthermore, the generation unit can generate a logical configuration diagram while taking into consideration network vulnerabilities in accordance with the security policy. For example, the generation unit can evaluate network vulnerabilities based on the security policy and generate a logical configuration diagram in which vulnerabilities are minimized. This allows the generation unit to generate a secure logical configuration diagram while taking into consideration the network security policy.

[0042] The generation unit can generate a logical configuration diagram while taking compatibility with other network management tools into consideration. For example, the generation unit can generate a logical configuration diagram while taking compatibility with other network management tools into consideration. For example, the generation unit can generate a logical configuration diagram in a format compatible with other network management tools, such as an SNMP tool or a network monitoring tool. The generation unit can also work with other network management tools to generate an optimal logical configuration diagram. For example, the generation unit can generate a logical configuration diagram while taking into consideration network topology and connection relationships, referring to information from other network management tools. Furthermore, the generation unit can adjust the method for generating the logical configuration diagram while taking compatibility with other network management tools into consideration. For example, the generation unit can work with other network management tools to determine the priority of information to be acquired in order to avoid information duplication. This allows the generation unit to facilitate inter-system collaboration while taking compatibility with other network management tools into consideration.

[0043] The display unit may add a function of highlighting important parts of the network when displaying the network. The display unit may, for example, highlight important devices or links of the network. For example, the display unit may highlight parts with heavy traffic or parts that are prone to failure. The display unit may also highlight important segments of the network. For example, the display unit may highlight the backbone or major routers of the network. Furthermore, the display unit may display important parts in different colors. For example, the display unit may display important devices or links in red, allowing the user to quickly grasp them. In this way, the display unit may highlight important parts of the network, allowing the user to quickly grasp important information.

[0044] When displaying information, the display unit can provide an optimal display layout by referring to the user's operation history. The display unit can provide an optimal display layout based on, for example, the user's past operation history. For example, the display unit can analyze the user's click history and operation time and prioritize displaying frequently used functions. The display unit can also customize the display layout based on the user's operation history. For example, the display unit can place information that the user frequently accesses in a prominent position, thereby improving operation efficiency. Furthermore, the display unit has an algorithm that analyzes the user's operation history and provides an optimal display layout. For example, the display unit can learn the user's operation patterns and provide a display layout that meets individual needs. As a result, the display unit can improve user convenience by providing an optimal display layout based on the user's operation history.

[0045] The display unit can take the geographical distribution of the network into consideration when displaying the information. For example, the display unit can prioritize displaying geographically close devices by taking the geographical distribution of the network into consideration. The display unit can also periodically display geographically distant devices. Furthermore, the display unit can adjust the frequency of display based on the geographical distribution. For example, the display unit can frequently display devices in the same region to optimize network traffic between regions. The display unit can also periodically display devices in different regions to grasp the overall network status. In this way, the display unit can make it easier for the user to grasp geographical information by displaying based on the geographical distribution of the network.

[0046] When displaying information, the display unit can integrate and display information from other network management systems. For example, the display unit can integrate and display information from other network management systems. For example, the display unit can work with other network management systems, such as an SNMP manager or a network monitoring tool, to avoid duplication of information. The display unit can also determine the priority of information to be displayed by referring to information from other network management systems. Furthermore, the display unit can achieve centralized management of information by working with other network management systems. For example, the display unit can integrate information from other network management systems and grasp the status of the entire network in a unified manner. As a result, the display unit can realize centralized management of information by integrating and displaying information from other network management systems.

[0047] The storage unit performs version management of data when storing the data, thereby facilitating comparison with past data. The storage unit, for example, can assign a version number to the data to be stored. For example, the storage unit can assign a version number to the generated logical configuration diagram, thereby facilitating comparison between past data and current data. The storage unit can also perform version management and store a data change history. For example, the storage unit can store a data change history and grasp the differences between past data and current data. Furthermore, the storage unit can maintain data consistency by performing version management. For example, the storage unit can perform version management of data and maintain data consistency. As a result, the storage unit can perform version management of data when storing the data, thereby facilitating comparison with past data.

[0048] The storage unit may compress or encrypt data when storing the data, thereby improving storage efficiency and security. For example, the storage unit may compress the data to be stored to improve storage efficiency. For example, the storage unit may compress the data using a compression algorithm to save storage space. The storage unit may also encrypt the data to be stored to improve security. For example, the storage unit may encrypt the data using an encryption algorithm to protect the data from unauthorized access. Furthermore, the storage unit may combine compression and encryption to improve storage efficiency and security. For example, the storage unit may compress the data and then encrypt it to save storage space while improving security. Thus, the storage unit may compress or encrypt the data when storing the data, thereby improving storage efficiency and security.

[0049] The storage unit can store data in a distributed manner taking into account the geographical distribution of the network. For example, the storage unit can store data preferentially in a geographically close server taking into account the geographical distribution of the network. The storage unit can also periodically store data in a geographically distant server. The storage unit can also adjust the frequency of data storage based on the geographical distribution. For example, the storage unit can frequently store data in a server within the same region to optimize data transfer between regions. The storage unit can also periodically store data in a server in a different region to ensure overall data redundancy. As a result, the storage unit can store data in a distributed manner based on the geographical distribution of the network, thereby enabling efficient data storage.

[0050] When storing data, the storage unit can coordinate data with other network management systems to achieve centralized data management. The storage unit can, for example, integrate and store data from other network management systems. For example, the storage unit can coordinate with other network management systems, such as an SNMP manager or a network monitoring tool, to avoid data duplication. The storage unit can also determine the priority of data to be stored by referring to data from other network management systems. Furthermore, the storage unit can achieve centralized data management by coordinating with other network management systems. For example, the storage unit can integrate data from other network management systems and centrally manage data for the entire network. As a result, the storage unit can coordinate data with other network management systems to achieve centralized data management, thereby enabling efficient data storage.

[0051] The comparison unit may add a function to visually display the differences between past data and current data when comparing. The comparison unit may, for example, display the differences between past data and current data in a graph. For example, the comparison unit may display the differences between past data and current data in a line graph or bar graph, allowing the user to intuitively grasp the changes. The comparison unit may also display the differences between past data and current data in different colors. For example, the comparison unit may display increased portions in green and decreased portions in red, allowing the user to understand the changes at a glance. Furthermore, the comparison unit may display the differences between past data and current data in an animation. For example, the comparison unit may display changes in the data in an animation, allowing the user to visually track the process of the changes. In this way, the comparison unit may visually display the differences between past data and current data, allowing the user to intuitively grasp the changes.

[0052] When making a comparison, the comparison unit can evaluate the reliability of the data and prioritize comparison of highly reliable data. The comparison unit can evaluate the reliability of the data based on, for example, the origin and consistency of the data. For example, the comparison unit can prioritize comparison of data from highly reliable devices to obtain accurate comparison results. The comparison unit can also evaluate the reliability of the data and exclude data with low reliability. Furthermore, the comparison unit can perform a detailed comparison based on highly reliable data. For example, the comparison unit can evaluate the consistency of the data and prioritize comparison of data with high consistency. The comparison unit can also evaluate the origin of the data and prioritize comparison of data from highly reliable devices. In this way, the comparison unit can evaluate the reliability of the data and prioritize comparison of highly reliable data to obtain accurate comparison results.

[0053] The comparison unit may compare data taking into account the geographical distribution of the network. For example, the comparison unit may prioritize comparison of data from geographically close devices taking into account the geographical distribution of the network. The comparison unit may also periodically compare data from geographically distant devices. Furthermore, the comparison unit may adjust the frequency of data comparison based on the geographical distribution. For example, the comparison unit may frequently compare data from devices in the same region to optimize network traffic between regions. The comparison unit may also periodically compare data from devices in different regions to grasp the overall network status. As a result, the comparison unit may perform efficient data comparison by comparing data based on the geographical distribution of the network.

[0054] When comparing data, the comparison unit can integrate and compare data from other network management systems. For example, the comparison unit can integrate and compare data from other network management systems. For example, the comparison unit can work with other network management systems, such as SNMP managers and network monitoring tools, to avoid data duplication. The comparison unit can also determine the priority of the data to be compared by referring to data from other network management systems. Furthermore, the comparison unit can achieve centralized management of information by working with other network management systems. For example, the comparison unit can integrate data from other network management systems and centrally manage data for the entire network. As a result, the comparison unit can integrate and compare data from other network management systems, thereby achieving centralized management of information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The data collection unit can acquire physical location information of devices within the network, and the generation unit can generate a physical configuration diagram based on that location information. For example, the data collection unit can acquire device location information using GPS or beacons. The generation unit can also generate a configuration diagram showing the physical layout of devices based on the acquired location information. Furthermore, the display unit can visually display the generated physical configuration diagram, allowing users to intuitively understand the physical layout of devices. This makes physical network management easier and enables quick response when a failure occurs.

[0057] The data collection unit acquires power consumption information of devices within the network, and the generation unit can also generate an energy consumption map based on that information. For example, the data collection unit can acquire information using sensors that monitor the power usage of devices. The generation unit can also generate a map showing the energy consumption status of the entire network based on the acquired power consumption information. Furthermore, the display unit can visually display the generated energy consumption map, making it easier for users to identify areas with high energy consumption. This can be expected to improve energy efficiency and reduce costs.

[0058] The generation unit can also automatically generate a maintenance schedule based on the operating time information of devices in the network. For example, the generation unit can analyze device operating time data and predict when maintenance will be required. The generation unit can also generate an optimal maintenance plan based on the predicted maintenance schedule. Furthermore, the display unit can visually display the generated maintenance schedule, allowing the user to easily understand the maintenance plan. This makes it possible to prevent device failures and maintain stable network operation.

[0059] The display unit may also provide a function that allows a user to add custom labels to the generated logical configuration diagram. For example, the display unit allows a user to assign arbitrary labels to specific devices or links. The display unit may also provide a filtering function based on the assigned labels, allowing the user to display only devices that have a specific label. Furthermore, the display unit may provide a color-coded display function for labels, allowing the user to easily visually distinguish devices according to the type of label. This allows the user to more intuitively understand the network configuration diagram.

[0060] The generation unit can also generate a security risk map based on the security status of devices within the network. For example, the generation unit can analyze the security patch application status and vulnerability information of devices to identify high-risk devices. The generation unit can also generate a security risk map for the entire network based on the identified risk information. Furthermore, the display unit can visually display the generated security risk map, allowing the user to quickly identify high-risk areas. This makes it easier to determine the priorities of security measures.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The data collection unit acquires information from each device in the network. The data collection unit acquires information from each device using, for example, SNMP (Simple Network Management Protocol) or API (Application Programming Interface). Specifically, it can acquire device status information and network traffic information using SNMP, and device configuration information and log information using API. Step 2: The generation unit analyzes the information collected by the data collection unit and generates a logical configuration diagram of the network. The generation unit analyzes the data using machine learning algorithms and automatically extracts the network topology and connections. For example, the analysis is performed using algorithms such as neural networks and decision trees. Step 3: The display unit visually displays the logical configuration diagram generated by the generator. The display unit displays the logical configuration diagram using a graph or chart display, and provides zoom-in / zoom-out and filtering functions. For example, the display unit can zoom in / zoom out using a mouse wheel or slider, and provides a filtering function to display only specific devices.

[0063] (Example 2) An automatic network configuration diagram generation system according to an embodiment of the present invention acquires information from each device in a network and automatically generates and displays a logical configuration diagram of the network using a generation AI. This system includes a data collection unit that acquires information from each device in the network, a generation unit that analyzes the collected information and generates a logical configuration diagram of the network, and a display unit that displays the generated logical configuration diagram. For example, the data collection unit acquires information from each device using SNMP or an API. The generation unit then analyzes the collected data using a machine learning algorithm to generate a logical configuration diagram of the network. Furthermore, the display unit visually displays the generated logical configuration diagram and provides zoom-in / zoom-out and filtering functions. This reduces the risk of prolonged system failures and reduces maintenance and operation costs. Furthermore, the system's innovability can be enhanced by adding a storage unit that stores the logical configuration diagrams generated by the generation unit and a comparison unit that compares them with previous configuration diagrams. This automatic network configuration diagram generation system reduces the risk of prolonged system failures and reduces maintenance and operation costs.

[0064] A network configuration diagram automatic generation system according to an embodiment includes a data collection unit, a generation unit, and a display unit. The data collection unit acquires information from each device in a network. The data collection unit acquires information from each device using, for example, Simple Network Management Protocol (SNMP) or an Application Programming Interface (API). For example, the data collection unit can acquire device status information and network traffic information using SNMP. The data collection unit can also acquire device configuration information and log information using an API. The generation unit analyzes the collected data using a machine learning algorithm to generate a logical configuration diagram of the network. The generation unit can analyze the data using a machine learning algorithm such as a neural network or a decision tree to automatically extract the network topology and connection relationships. The generation unit generates the logical configuration diagram of the network based on the collected data. The display unit visually displays the generated logical configuration diagram. The display unit can display the logical configuration diagram using, for example, a graph or chart. The display unit also provides zoom-in / zoom-out functions and filtering functions. For example, the display unit can zoom in and out of the logical configuration diagram using a mouse wheel or slider. The display unit can also provide a filtering function that displays only specific devices. As a result, the automatic network configuration diagram generation system according to the embodiment can automatically generate a logical configuration diagram of a network, reduce the risk of prolonged system failures, and reduce maintenance and operation costs.

[0065] The data collection unit can acquire information from each device using SNMP or an API. For example, the data collection unit can acquire device status information and network traffic information using versions such as SNMPv2 and SNMPv3. The data collection unit can also acquire device configuration information and log information using types such as REST API and SOAP API. For example, the data collection unit can acquire a device's CPU usage rate and memory usage rate using SNMPv2. The data collection unit can also acquire device configuration information using a REST API. Furthermore, the data collection unit can acquire device log information using a SOAP API. This allows the data collection unit to efficiently acquire information from each device by using SNMP or an API.

[0066] The generation unit can analyze the collected data using a machine learning algorithm and generate a logical configuration diagram. The generation unit can analyze the data using a machine learning algorithm, such as a neural network or a decision tree, and automatically extract the network topology and connection relationships. For example, the generation unit can analyze the connection relationships between devices using a neural network and generate a logical configuration diagram of the network. The generation unit can also analyze device status information using a decision tree and generate a network topology. Furthermore, the generation unit can group devices using a clustering algorithm and generate a logical configuration diagram of the network. For example, the generation unit can group devices using K-means clustering and generate a logical configuration diagram of the network. In this way, the generation unit can efficiently analyze the collected data and generate an accurate logical configuration diagram by using a machine learning algorithm.

[0067] The display unit visually displays the generated logical configuration diagram and can provide zoom-in / zoom-out and filtering functions. The display unit can display the logical configuration diagram using, for example, a graph or chart. For example, the display unit can display a graph of the network topology to visually display the connection relationships between devices. The display unit can also display a chart of network traffic information to visually display traffic fluctuations. The display unit also provides zoom-in / zoom-out functions. For example, the display unit can zoom in and out of the logical configuration diagram using a mouse wheel or a slider. The display unit also provides filtering functions. For example, the display unit can provide a filtering function to display only specific devices. This allows the display unit to visually display the generated logical configuration diagram and make it easier for the user to intuitively understand the logical configuration diagram.

[0068] The generation unit may include a storage unit that stores the generated logical configuration diagram. The storage unit may store the generated logical configuration diagram in a database, for example. For example, the storage unit may store the generated logical configuration diagram using an SQL database. The storage unit may also store the generated logical configuration diagram in a file. For example, the storage unit may store the generated logical configuration diagram in an XML file or a JSON file. The storage unit may also store the generated logical configuration diagram in cloud storage. For example, the storage unit may store the generated logical configuration diagram using a cloud storage service. In this way, the generation unit can store the generated logical configuration diagram for later reference.

[0069] The generation unit may include a comparison unit that compares the current configuration diagram with a past configuration diagram. The comparison unit can, for example, calculate the difference from past data and grasp changes in the network. For example, the comparison unit can compare the current configuration diagram with the past configuration diagram to detect addition or deletion of devices, changes in connection relationships, etc. The comparison unit can also compare specific parameters. For example, the comparison unit can compare device status information and network traffic information to detect abnormal fluctuations. Furthermore, the comparison unit can visually compare the current configuration diagram with the past configuration diagram. For example, the comparison unit can overlay the current configuration diagram with the past configuration diagram to visually grasp changes. This allows the generation unit to more easily grasp changes in the network by comparing with the past configuration diagram.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of acquiring device information based on the estimated user's emotions. The data collection unit can estimate the user's emotions using, for example, facial expression recognition or voice analysis. For example, the data collection unit can capture the user's facial expressions using a camera and estimate the user's emotions using a facial expression recognition algorithm. The data collection unit can also record the user's voice using a microphone and estimate the user's emotions using a voice analysis algorithm. Furthermore, the data collection unit can adjust the timing of acquiring device information based on the estimated user's emotions. For example, if the user is feeling stressed, the data collection unit can reduce the frequency of acquiring device information to reduce system load. Furthermore, if the user is relaxed, the data collection unit can increase the frequency of acquiring device information to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize acquiring important device information and perform quick analysis. In this way, the data collection unit can optimize system load by adjusting the timing of acquiring device information according to the user's emotions.

[0071] The data collection unit can monitor network traffic conditions in real time and acquire device information at appropriate times. The data collection unit can monitor network traffic conditions in real time using, for example, packet capture or traffic monitoring tools. For example, the data collection unit can collect packets on the network using packet capture and monitor traffic fluctuations. The data collection unit can also monitor network traffic conditions in real time using a traffic monitoring tool. Furthermore, the data collection unit can adjust the timing of acquiring device information according to network traffic conditions. For example, the data collection unit can intensively acquire device information during times of low traffic. Furthermore, the data collection unit can acquire only important device information during times of high traffic. Furthermore, the data collection unit can predict traffic fluctuations and acquire device information at optimal timing. As a result, the data collection unit can acquire device information according to network traffic conditions, thereby efficiently collecting information.

[0072] The data collection unit can determine the priority of information to be acquired based on the device's operating status or error information. The data collection unit can monitor the operating status of the device, such as the CPU usage rate and memory usage rate, and prioritize acquiring information from operating devices. The data collection unit can also monitor error information such as log files and error messages and acquire detailed information from devices in which errors are occurring. The data collection unit can also periodically acquire information from devices with unstable operating status. For example, the data collection unit can prioritize acquiring information from devices with high CPU usage rates and monitor network load. The data collection unit can also acquire detailed log information from devices in which error messages are frequently occurring and identify the cause of the problem. In this way, the data collection unit can prioritize acquiring important information by determining the priority of information based on the device's operating status and error information.

[0073] The data collection unit can estimate a user's emotion and select the type of device information to acquire based on the estimated user's emotion. The data collection unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis. For example, the data collection unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The data collection unit can also record the user's voice using a microphone and estimate the user's emotion using a voice analysis algorithm. Furthermore, the data collection unit can select the type of device information to acquire based on the estimated user's emotion. For example, the data collection unit can acquire only important device information when the user is stressed. Furthermore, the data collection unit can acquire detailed device information when the user is relaxed. Furthermore, the data collection unit can acquire device information that can be analyzed quickly when the user is in a hurry. In this way, the data collection unit can collect appropriate information by selecting the type of device information to acquire based on the user's emotion.

[0074] When acquiring device information, the data collection unit can take the geographical distribution of the network into consideration when collecting information. For example, the data collection unit can prioritize acquiring information from geographically close devices by taking the geographical distribution of the network into consideration. The data collection unit can also periodically acquire information from geographically distant devices. Furthermore, the data collection unit can adjust the frequency of information acquisition based on the geographical distribution. For example, the data collection unit can frequently acquire information from devices in the same region to optimize network traffic between regions. The data collection unit can also periodically acquire information from devices in different regions to grasp the overall network status. In this way, the data collection unit can efficiently collect information by collecting information based on the geographical distribution of the network.

[0075] When acquiring device information, the data collection unit can cooperate with other network management systems to realize centralized information management. The data collection unit can, for example, integrate and collect information from other network management systems. For example, the data collection unit can cooperate with other network management systems, such as an SNMP manager or a network monitoring tool, to avoid information duplication. The data collection unit can also determine the priority of information to be acquired by referring to information from other network management systems. Furthermore, the data collection unit can realize centralized information management by coordinating with other network management systems. For example, the data collection unit can integrate information from other network management systems and centrally grasp the status of the entire network. As a result, the data collection unit can cooperate with other network management systems to realize centralized information management, thereby enabling efficient information collection.

[0076] The generation unit can estimate the user's emotion and adjust the generation method of the logical configuration diagram based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis. For example, the generation unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The generation unit can also record the user's voice using a microphone and estimate the user's emotion using a voice analysis algorithm. Furthermore, the generation unit can adjust the generation method of the logical configuration diagram based on the estimated user's emotion. For example, the generation unit can generate a detailed logical configuration diagram when the user is relaxed. Furthermore, the generation unit can generate a simplified logical configuration diagram when the user is in a hurry. Furthermore, the generation unit can generate a visually easy-to-understand logical configuration diagram when the user is feeling stressed. In this way, the generation unit can generate an optimal logical configuration diagram for the user by adjusting the generation method of the logical configuration diagram according to the user's emotion.

[0077] The generation unit can evaluate the reliability of the collected data and generate a logical configuration diagram by preferentially using highly reliable data. The generation unit can evaluate the reliability of the data based on, for example, the origin and consistency of the data. For example, the generation unit can generate a detailed logical configuration diagram by preferentially using data from highly reliable devices. The generation unit can also evaluate the reliability of the data and exclude low-reliability data. Furthermore, the generation unit can accurately extract a network topology and connection relationships based on the highly reliable data. For example, the generation unit can evaluate the consistency of the data and preferentially use data with high consistency. The generation unit can also evaluate the origin of the data and preferentially use data from highly reliable devices. In this way, the generation unit can generate an accurate logical configuration diagram by evaluating the reliability of the collected data and preferentially using the highly reliable data to generate the logical configuration diagram.

[0078] The generation unit can generate an optimal logical configuration diagram by taking into account the network topology and the interrelationships of devices. The generation unit can generate an optimal logical configuration diagram based on, for example, the network topology. For example, the generation unit can generate a logical configuration diagram of a network by taking into account topologies such as a star type or a mesh type. The generation unit can also generate a detailed logical configuration diagram by taking into account the interrelationships of devices. For example, the generation unit can generate a logical configuration diagram of a network by taking into account the connection relationships and dependencies between devices. Furthermore, the generation unit can generate an optimal logical configuration diagram by integrating the network topology and the interrelationships of devices. For example, the generation unit can generate a logical configuration diagram of the entire network based on the network topology and the interrelationships of devices. In this way, the generation unit can generate an optimal logical configuration diagram by taking into account the network topology and the interrelationships of devices.

[0079] The generation unit can estimate the user's emotion and adjust the level of detail of the generated logical configuration diagram based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis. For example, the generation unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The generation unit can also record the user's voice using a microphone and estimate the user's emotion using a voice analysis algorithm. Furthermore, the generation unit can adjust the level of detail of the generated logical configuration diagram based on the estimated user's emotion. For example, the generation unit can generate a detailed logical configuration diagram when the user is relaxed. Furthermore, the generation unit can generate a simplified logical configuration diagram when the user is in a hurry. Furthermore, the generation unit can generate a visually easy-to-understand logical configuration diagram when the user is feeling stressed. In this way, the generation unit can adjust the level of detail of the generated logical configuration diagram according to the user's emotion, thereby generating a logical configuration diagram with an optimal level of detail for the user.

[0080] The generation unit can generate the logical configuration diagram while taking into consideration the network security policy. The generation unit can generate the logical configuration diagram, for example, based on the network security policy. For example, the generation unit can generate the logical configuration diagram while taking into consideration security policies such as access control and data encryption. The generation unit can also generate a detailed logical configuration diagram while taking into consideration the security policy. For example, the generation unit can generate an optimal logical configuration diagram while taking into consideration the network topology and connection relationships based on the security policy. Furthermore, the generation unit can generate a logical configuration diagram while taking into consideration network vulnerabilities in accordance with the security policy. For example, the generation unit can evaluate network vulnerabilities based on the security policy and generate a logical configuration diagram in which vulnerabilities are minimized. This allows the generation unit to generate a secure logical configuration diagram while taking into consideration the network security policy.

[0081] The generation unit can generate a logical configuration diagram while taking compatibility with other network management tools into consideration. For example, the generation unit can generate a logical configuration diagram while taking compatibility with other network management tools into consideration. For example, the generation unit can generate a logical configuration diagram in a format compatible with other network management tools, such as an SNMP tool or a network monitoring tool. The generation unit can also work with other network management tools to generate an optimal logical configuration diagram. For example, the generation unit can generate a logical configuration diagram while taking into consideration network topology and connection relationships, referring to information from other network management tools. Furthermore, the generation unit can adjust the method for generating the logical configuration diagram while taking compatibility with other network management tools into consideration. For example, the generation unit can work with other network management tools to determine the priority of information to be acquired in order to avoid information duplication. This allows the generation unit to facilitate inter-system collaboration while taking compatibility with other network management tools into consideration.

[0082] The display unit can estimate a user's emotion and adjust the display method of the logical configuration diagram based on the estimated user's emotion. The display unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis. For example, the display unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The display unit can also record the user's voice using a microphone and estimate the user's emotion using a voice analysis algorithm. Furthermore, the display unit can adjust the display method of the logical configuration diagram based on the estimated user's emotion. For example, the display unit can provide a simple, highly visible display method when the user is nervous. Furthermore, the display unit can provide a display method including detailed information when the user is relaxed. Furthermore, the display unit can provide a display method that focuses on the main points when the user is in a hurry. In this way, the display unit can adjust the display method according to the user's emotion, thereby enabling the optimal display for the user.

[0083] The display unit may add a function of highlighting important parts of the network when displaying the network. The display unit may, for example, highlight important devices or links of the network. For example, the display unit may highlight parts with heavy traffic or parts that are prone to failure. The display unit may also highlight important segments of the network. For example, the display unit may highlight the backbone or major routers of the network. Furthermore, the display unit may display important parts in different colors. For example, the display unit may display important devices or links in red, allowing the user to quickly grasp them. In this way, the display unit may highlight important parts of the network, allowing the user to quickly grasp important information.

[0084] When displaying information, the display unit can provide an optimal display layout by referring to the user's operation history. The display unit can provide an optimal display layout based on, for example, the user's past operation history. For example, the display unit can analyze the user's click history and operation time and prioritize displaying frequently used functions. The display unit can also customize the display layout based on the user's operation history. For example, the display unit can place information that the user frequently accesses in a prominent position, thereby improving operation efficiency. Furthermore, the display unit has an algorithm that analyzes the user's operation history and provides an optimal display layout. For example, the display unit can learn the user's operation patterns and provide a display layout that meets individual needs. As a result, the display unit can improve user convenience by providing an optimal display layout based on the user's operation history.

[0085] The display unit can estimate the user's emotions and determine the priority of information to be displayed based on the estimated user's emotions. The display unit can estimate the user's emotions using, for example, facial expression recognition or voice analysis. For example, the display unit can capture the user's facial expression using a camera and estimate the user's emotions using a facial expression recognition algorithm. The display unit can also record the user's voice using a microphone and estimate the user's emotions using a voice analysis algorithm. Furthermore, the display unit can determine the priority of information to be displayed based on the estimated user's emotions. For example, the display unit can prioritize displaying important information when the user is nervous. The display unit can prioritize displaying detailed information when the user is relaxed. Furthermore, the display unit can prioritize displaying information that is concise when the user is in a hurry. In this way, the display unit can prioritize displaying information that is important to the user by determining the priority of information according to the user's emotions.

[0086] The display unit can take the geographical distribution of the network into consideration when displaying the information. For example, the display unit can prioritize displaying geographically close devices by taking the geographical distribution of the network into consideration. The display unit can also periodically display geographically distant devices. Furthermore, the display unit can adjust the frequency of display based on the geographical distribution. For example, the display unit can frequently display devices in the same region to optimize network traffic between regions. The display unit can also periodically display devices in different regions to grasp the overall network status. In this way, the display unit can make it easier for the user to grasp geographical information by displaying based on the geographical distribution of the network.

[0087] When displaying information, the display unit can integrate and display information from other network management systems. For example, the display unit can integrate and display information from other network management systems. For example, the display unit can work with other network management systems, such as an SNMP manager or a network monitoring tool, to avoid duplication of information. The display unit can also determine the priority of information to be displayed by referring to information from other network management systems. Furthermore, the display unit can achieve centralized management of information by working with other network management systems. For example, the display unit can integrate information from other network management systems and grasp the status of the entire network in a unified manner. As a result, the display unit can realize centralized management of information by integrating and displaying information from other network management systems.

[0088] The storage unit can estimate a user's emotion and select data to be stored based on the estimated user's emotion. The storage unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis. For example, the storage unit can capture a user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The storage unit can also record the user's voice using a microphone and estimate the user's emotion using a voice analysis algorithm. Furthermore, the storage unit can select data to be stored based on the estimated user's emotion. For example, the storage unit can store only important data when the user is feeling stressed. The storage unit can also store detailed data when the user is relaxed. Furthermore, the storage unit can store data that can be analyzed quickly when the user is in a hurry. In this way, the storage unit can store appropriate data by selecting data to be stored according to the user's emotion.

[0089] The storage unit performs version management of data when storing the data, thereby facilitating comparison with past data. The storage unit, for example, can assign a version number to the data to be stored. For example, the storage unit can assign a version number to the generated logical configuration diagram, thereby facilitating comparison between past data and current data. The storage unit can also perform version management and store a data change history. For example, the storage unit can store a data change history and grasp the differences between past data and current data. Furthermore, the storage unit can maintain data consistency by performing version management. For example, the storage unit can perform version management of data and maintain data consistency. As a result, the storage unit can perform version management of data when storing the data, thereby facilitating comparison with past data.

[0090] The storage unit may compress or encrypt data when storing the data, thereby improving storage efficiency and security. For example, the storage unit may compress the data to be stored to improve storage efficiency. For example, the storage unit may compress the data using a compression algorithm to save storage space. The storage unit may also encrypt the data to be stored to improve security. For example, the storage unit may encrypt the data using an encryption algorithm to protect the data from unauthorized access. Furthermore, the storage unit may combine compression and encryption to improve storage efficiency and security. For example, the storage unit may compress the data and then encrypt it to save storage space while improving security. Thus, the storage unit may compress or encrypt the data when storing the data, thereby improving storage efficiency and security.

[0091] The storage unit can estimate a user's emotion and determine the priority of data to be stored based on the estimated user's emotion. The storage unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis. For example, the storage unit can capture a user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The storage unit can also record the user's voice using a microphone and estimate the user's emotion using a voice analysis algorithm. Furthermore, the storage unit can determine the priority of data to be stored based on the estimated user's emotion. For example, the storage unit can prioritize storing important data when the user is nervous. The storage unit can prioritize storing detailed data when the user is relaxed. Furthermore, the storage unit can prioritize storing data that summarizes the main points when the user is in a hurry. In this way, the storage unit can prioritize storing important data by determining the priority of data to be stored according to the user's emotion.

[0092] The storage unit can store data in a distributed manner taking into account the geographical distribution of the network. For example, the storage unit can store data preferentially in a geographically close server taking into account the geographical distribution of the network. The storage unit can also periodically store data in a geographically distant server. The storage unit can also adjust the frequency of data storage based on the geographical distribution. For example, the storage unit can frequently store data in a server within the same region to optimize data transfer between regions. The storage unit can also periodically store data in a server in a different region to ensure overall data redundancy. As a result, the storage unit can store data in a distributed manner based on the geographical distribution of the network, thereby enabling efficient data storage.

[0093] When storing data, the storage unit can coordinate data with other network management systems to achieve centralized data management. The storage unit can, for example, integrate and store data from other network management systems. For example, the storage unit can coordinate with other network management systems, such as an SNMP manager or a network monitoring tool, to avoid data duplication. The storage unit can also determine the priority of data to be stored by referring to data from other network management systems. Furthermore, the storage unit can achieve centralized data management by coordinating with other network management systems. For example, the storage unit can integrate data from other network management systems and centrally manage data for the entire network. As a result, the storage unit can coordinate data with other network management systems to achieve centralized data management, thereby enabling efficient data storage.

[0094] The comparison unit can estimate the user's emotion and select data to compare based on the estimated user's emotion. The comparison unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis. For example, the comparison unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The comparison unit can also record the user's voice using a microphone and estimate the user's emotion using a voice analysis algorithm. Furthermore, the comparison unit can select data to compare based on the estimated user's emotion. For example, the comparison unit can compare only important data when the user is stressed. The comparison unit can also compare detailed data when the user is relaxed. Furthermore, the comparison unit can compare data that can be analyzed quickly when the user is in a hurry. In this way, the comparison unit can compare appropriate data by selecting data to compare based on the user's emotion.

[0095] The comparison unit may add a function to visually display the differences between past data and current data when comparing. The comparison unit may, for example, display the differences between past data and current data in a graph. For example, the comparison unit may display the differences between past data and current data in a line graph or bar graph, allowing the user to intuitively grasp the changes. The comparison unit may also display the differences between past data and current data in different colors. For example, the comparison unit may display increased portions in green and decreased portions in red, allowing the user to understand the changes at a glance. Furthermore, the comparison unit may display the differences between past data and current data in an animation. For example, the comparison unit may display changes in the data in an animation, allowing the user to visually track the process of the changes. In this way, the comparison unit may visually display the differences between past data and current data, allowing the user to intuitively grasp the changes.

[0096] When making a comparison, the comparison unit can evaluate the reliability of the data and prioritize comparison of highly reliable data. The comparison unit can evaluate the reliability of the data based on, for example, the origin and consistency of the data. For example, the comparison unit can prioritize comparison of data from highly reliable devices to obtain accurate comparison results. The comparison unit can also evaluate the reliability of the data and exclude data with low reliability. Furthermore, the comparison unit can perform a detailed comparison based on highly reliable data. For example, the comparison unit can evaluate the consistency of the data and prioritize comparison of data with high consistency. The comparison unit can also evaluate the origin of the data and prioritize comparison of data from highly reliable devices. In this way, the comparison unit can evaluate the reliability of the data and prioritize comparison of highly reliable data to obtain accurate comparison results.

[0097] The comparison unit can estimate the user's emotion and adjust the display method of the comparison result based on the estimated user's emotion. The comparison unit can estimate the user's emotion using, for example, facial expression recognition or voice analysis. For example, the comparison unit can capture the user's facial expression using a camera and estimate the user's emotion using a facial expression recognition algorithm. The comparison unit can also record the user's voice using a microphone and estimate the user's emotion using a voice analysis algorithm. Furthermore, the comparison unit can adjust the display method of the comparison result based on the estimated user's emotion. For example, the comparison unit can provide a simple, highly visible display method when the user is nervous. Furthermore, the comparison unit can provide a display method including detailed information when the user is relaxed. Furthermore, the comparison unit can provide a display method that focuses on the main points when the user is in a hurry. In this way, the comparison unit can adjust the display method according to the user's emotion, thereby enabling the optimal display for the user.

[0098] The comparison unit may compare data taking into account the geographical distribution of the network. For example, the comparison unit may prioritize comparison of data from geographically close devices taking into account the geographical distribution of the network. The comparison unit may also periodically compare data from geographically distant devices. Furthermore, the comparison unit may adjust the frequency of data comparison based on the geographical distribution. For example, the comparison unit may frequently compare data from devices in the same region to optimize network traffic between regions. The comparison unit may also periodically compare data from devices in different regions to grasp the overall network status. As a result, the comparison unit may perform efficient data comparison by comparing data based on the geographical distribution of the network.

[0099] When comparing data, the comparison unit can integrate and compare data from other network management systems. For example, the comparison unit can integrate and compare data from other network management systems. For example, the comparison unit can work with other network management systems, such as SNMP managers and network monitoring tools, to avoid data duplication. The comparison unit can also determine the priority of the data to be compared by referring to data from other network management systems. Furthermore, the comparison unit can achieve centralized management of information by working with other network management systems. For example, the comparison unit can integrate data from other network management systems and centrally manage data for the entire network. As a result, the comparison unit can integrate and compare data from other network management systems, thereby achieving centralized management of information. === Hard Collateral 1-1 === Each of the multiple elements, including the data collection unit, generation unit, display unit, storage unit, and comparison unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit acquires information from each device using the communication I / F 44 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a machine learning algorithm to generate a logical configuration diagram of the network. The display unit visually displays the generated logical configuration diagram via the output device 40 of the smart device 14. The storage unit saves the logical configuration diagram in the storage 32 of the data processing device 12. The comparison unit compares the logical configuration diagram with a previous configuration diagram via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned data collection unit, generation unit, display unit, storage unit, and comparison unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit acquires information from each device using the communication I / F 44 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a machine learning algorithm to generate a logical configuration diagram of the network. The display unit visually displays the generated logical configuration diagram by the speaker 240 of the smart glasses 214. The storage unit stores the logical configuration diagram in the storage 32 of the data processing device 12. The comparison unit compares it with a previous configuration diagram by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned data collection unit, generation unit, display unit, storage unit, and comparison unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the data collection unit acquires information from each device using the communication I / F 44 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a machine learning algorithm to generate a logical configuration diagram of the network. The display unit visually displays the generated logical configuration diagram on the display 343 of the headset type terminal 314. The storage unit saves the logical configuration diagram in the storage 32 of the data processing device 12. The comparison unit compares it with a previous configuration diagram by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the data collection unit, generation unit, display unit, storage unit, and comparison unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit acquires information from each device using the communication I / F 44 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a machine learning algorithm to generate a logical configuration diagram of the network. The display unit visually displays the generated logical configuration diagram using LEDs or a display in the eye of the robot 414. The storage unit saves the logical configuration diagram in the storage 32 of the data processing device 12. The comparison unit compares it with a previous configuration diagram using the specific processing unit 290 of the data processing device 12.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The data collection unit can acquire physical location information of devices within the network, and the generation unit can generate a physical configuration diagram based on that location information. For example, the data collection unit can acquire device location information using GPS or beacons. The generation unit can also generate a configuration diagram showing the physical layout of devices based on the acquired location information. Furthermore, the display unit can visually display the generated physical configuration diagram, allowing users to intuitively understand the physical layout of devices. This makes physical network management easier and enables quick response when a failure occurs.

[0102] The data collection unit acquires power consumption information of devices within the network, and the generation unit can also generate an energy consumption map based on that information. For example, the data collection unit can acquire information using sensors that monitor the power usage of devices. The generation unit can also generate a map showing the energy consumption status of the entire network based on the acquired power consumption information. Furthermore, the display unit can visually display the generated energy consumption map, making it easier for users to identify areas with high energy consumption. This can be expected to improve energy efficiency and reduce costs.

[0103] The generation unit can also automatically generate a maintenance schedule based on the operating time information of devices in the network. For example, the generation unit can analyze device operating time data and predict when maintenance will be required. The generation unit can also generate an optimal maintenance plan based on the predicted maintenance schedule. Furthermore, the display unit can visually display the generated maintenance schedule, allowing the user to easily understand the maintenance plan. This makes it possible to prevent device failures and maintain stable network operation.

[0104] The display unit may also provide a function that allows a user to add custom labels to the generated logical configuration diagram. For example, the display unit allows a user to assign arbitrary labels to specific devices or links. The display unit may also provide a filtering function based on the assigned labels, allowing the user to display only devices that have a specific label. Furthermore, the display unit may provide a color-coded display function for labels, allowing the user to easily visually distinguish devices according to the type of label. This allows the user to more intuitively understand the network configuration diagram.

[0105] The generation unit can also generate a security risk map based on the security status of devices within the network. For example, the generation unit can analyze the security patch application status and vulnerability information of devices to identify high-risk devices. The generation unit can also generate a security risk map for the entire network based on the identified risk information. Furthermore, the display unit can visually display the generated security risk map, allowing the user to quickly identify high-risk areas. This makes it easier to determine the priorities of security measures.

[0106] The data collection unit can estimate the user's emotions and adjust the method of acquiring device information based on the estimated user's emotions. For example, if the user is feeling stressed, the data collection unit can reduce the frequency of acquiring device information to reduce the system load. Also, if the user is relaxed, the data collection unit can acquire detailed device information. Furthermore, if the user is in a hurry, the data collection unit can prioritize acquiring important device information and quickly analyze it. In this way, the data collection unit can optimize the system load by adjusting the method of acquiring device information according to the user's emotions.

[0107] The generation unit can estimate the user's emotions and adjust the layout of the generated logical configuration diagram based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible layout can be provided. If the user is relaxed, a layout including detailed information can be provided. Furthermore, if the user is in a hurry, a layout that focuses on the main points can be provided. In this way, the generation unit can adjust the layout of the logical configuration diagram according to the user's emotions, thereby enabling the optimal display for the user.

[0108] The display unit can estimate the user's emotions and adjust the level of detail of the information to be displayed based on the estimated user's emotions. For example, if the user is feeling stressed, only important information can be displayed. If the user is relaxed, detailed information can be displayed. If the user is in a hurry, information that focuses on the main points can be displayed. In this way, the display unit can adjust the level of detail of the information to be displayed according to the user's emotions, thereby providing the user with the most appropriate information.

[0109] The storage unit can estimate the user's emotion and select a format of data to be stored based on the estimated user's emotion. For example, if the user is stressed, the data can be stored in a simplified format. If the user is relaxed, the data can be stored in a detailed format. Furthermore, if the user is in a hurry, the data can be stored in a format that allows quick access. Thus, the storage unit can store data appropriately by selecting a format of data to be stored according to the user's emotion.

[0110] The comparison unit can estimate the user's emotions and adjust the display method of the comparison results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the comparison unit can adjust the display method according to the user's emotions, thereby enabling the optimal display for the user.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The data collection unit acquires information from each device in the network. The data collection unit acquires information from each device using, for example, SNMP (Simple Network Management Protocol) or API (Application Programming Interface). Specifically, it can acquire device status information and network traffic information using SNMP, and device configuration information and log information using API. Step 2: The generation unit analyzes the information collected by the data collection unit and generates a logical configuration diagram of the network. The generation unit analyzes the data using machine learning algorithms and automatically extracts the network topology and connections. For example, the analysis is performed using algorithms such as neural networks and decision trees. Step 3: The display unit visually displays the logical configuration diagram generated by the generator. The display unit displays the logical configuration diagram using a graph or chart display, and provides zoom-in / zoom-out and filtering functions. For example, the display unit can zoom in / zoom out using a mouse wheel or slider, and provides a filtering function to display only specific devices.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] 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.

[0142] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 data collection unit that acquires information from each device in the network; a generation unit that analyzes the information collected by the data collection unit and generates a logical configuration diagram of the network; a display unit that displays the logical configuration diagram generated by the generation unit; Equipped with A system characterized by:

2. The data collection unit Use SNMP or API to obtain information from each device The system of claim 1 .

3. The generation unit Analyze collected data using machine learning algorithms and generate logical diagrams The system of claim 1 .

4. The display unit Visually display the generated logical diagram and provide zoom-in / zoom-out and filtering capabilities The system of claim 1 .

5. The generation unit A storage unit is provided to store the generated logical configuration diagram. The system of claim 1 .

6. The generation unit Equipped with a comparison section for comparing with past configuration diagrams The system of claim 1 .

7. The data collection unit The system estimates the user's emotions and adjusts the timing of acquiring device information based on the estimated user emotions. The system of claim 1 .

8. The data collection unit Monitor network traffic conditions in real time and obtain device information at the appropriate time The system of claim 1 .

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