system

Abstract

An object of a system according to an embodiment is to provide the latest NW map in real time.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects NW information obtained from the NW device. The analysis unit understands and analyzes the connection rule based on the NW information collected by the collection unit. The generation unit generates a NW map using natural language processing based on a result of the analysis by the analysis unit. The provision unit immediately provides the NW map generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to provide the latest network maps in real time, which poses a risk of affecting network design and maintenance work.

[0005] The system according to the embodiment aims to provide the latest NW map in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects network information obtained from network devices. The analysis unit understands and analyzes connection rules based on the network information collected by the collection unit. The generation unit generates a network map using natural language processing based on the results of the analysis by the analysis unit. The provision unit immediately provides the network map generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide the latest NW map in real time. [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) A system according to an embodiment of the present invention aims to improve the efficiency of network design and maintenance in the telecommunications industry. This system collects network information obtained from network equipment, uses a large-scale language model (LLM) to understand and analyze advanced connection rules, and generates and provides a network map using natural language processing in real time. This allows the system to provide the latest network map in real time, thereby improving the efficiency of network design and maintenance. For example, the system collects network information obtained from network equipment, and the LLM analyzes it after understanding the advanced connection rules. Next, the LLM generates a network map using natural language processing, which is updated in real time. This system is used in network design and maintenance. Furthermore, the completion of an LLM specialized for the network domain enables automatic fault resolution. For example, this system enables automatic operation when a fault occurs, detection and automatic correction of network information configuration errors, and support for network design and maintenance through question-and-answer sessions with the LLM.

[0029] A network design and maintenance system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects network information obtained from network devices. The collection unit collects, for example, network traffic data, connection logs, and configuration information from the network devices. The collection unit can also collect network information based on collection frequency and selection criteria for collection targets. The analysis unit understands and analyzes connection rules based on the network information collected by the collection unit. The analysis unit understands advanced connection rules using, for example, LLM and analyzes the collected network information. The analysis unit can also perform analysis based on the algorithm used and the purpose of the analysis. The generation unit generates a network map using natural language processing based on the results of the analysis by the analysis unit. The generation unit generates the network map using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generation unit can also generate the network map in the form of a topology map, a connectivity map, or the like. The provision unit immediately provides the network map generated by the generation unit. The providing unit provides the network map based on, for example, the delay time and the means of providing. The providing unit can also provide the network map through a web application or a mobile application. As a result, the network design and maintenance system according to the embodiment can provide the latest network map in real time by collecting, analyzing, generating, and providing information obtained from network devices.

[0030] The analysis unit can perform automatic operation when a failure occurs. The analysis unit performs automatic operation depending on the type of failure, for example. For example, the analysis unit detects a network failure and automatically restarts or resets the system. The analysis unit can also identify the cause of the failure and execute appropriate response procedures. For example, the analysis unit can identify the location of the failure and change the settings of the relevant network equipment. The analysis unit can also evaluate the extent of the failure's impact and take measures to minimize the impact. For example, the analysis unit can set up a route to bypass the network equipment affected by the failure. This enables rapid response to the failure by automatically responding when a failure occurs.

[0031] The analysis unit can detect configuration errors in network information and perform automatic corrections. For example, the analysis unit detects erroneous configuration items and automatically corrects them. For example, the analysis unit analyzes configuration information of network devices and identifies erroneous settings. The analysis unit can also detect configuration errors based on an error detection method. For example, the analysis unit checks the consistency of configuration information and detects inconsistent settings. The analysis unit can also perform automatic corrections based on a correction procedure. For example, the analysis unit corrects erroneous settings and changes them to correct settings. The analysis unit can also correct configuration errors based on the scope of application of the correction. For example, the analysis unit corrects configuration errors in specific network devices. This automatically corrects configuration errors in network information, thereby improving the stability of the network.

[0032] The provisioning unit can provide support in the area of ​​network design and maintenance through question and answer sessions with the LLM. For example, the provisioning unit uses the LLM to provide answers to questions about network design and maintenance. For example, the provisioning unit inputs a question such as "Please tell me how to configure this network device" into the LLM, and the LLM generates an appropriate answer. The provisioning unit can also provide support based on the question and answer process. For example, the provisioning unit provides information about network design and maintenance through dialogue with the LLM. The provisioning unit can also conduct question and answer sessions based on the type of LLM used. For example, the provisioning unit answers questions about network design and maintenance using a specific LLM. This improves the efficiency of network design and maintenance through question and answer sessions with the LLM.

[0033] The collection unit can analyze past network information collection history and select an appropriate collection method. For example, the collection unit analyzes past collection history and selects the most efficient collection method. For example, the collection unit selects the optimal collection method for a specific time period from the past collection history. The collection unit can also find areas for improvement in the collection method and optimize it. For example, the collection unit analyzes past collection history and identifies areas for improvement in the collection method. The collection unit can also select an appropriate collection method based on the collection means and collection process. For example, the collection unit efficiently collects network information using a specific collection means. In this way, collection efficiency is improved by selecting the optimal collection method based on the past collection history.

[0034] When collecting network information, the collection unit can perform filtering based on the type and version of network equipment. For example, the collection unit filters and collects only necessary information based on the type of network equipment. For example, the collection unit collects related information based on the type of specific network equipment. The collection unit can also preferentially collect the latest information based on the version of the network equipment. For example, the collection unit collects information based on the latest version of network equipment. The collection unit can also filter and collect optimal information by combining the type and version of network equipment. For example, the collection unit collects information based on the type and version of specific network equipment. In this way, filtering based on the type and version of network equipment can efficiently collect only necessary information.

[0035] When collecting network information, the collection unit can select an appropriate collection means according to the operating status of the network equipment. For example, when the network equipment is in a high-load state, the collection unit selects a collection means that reduces the load. For example, the collection unit monitors the load of the network equipment, and when the load is high, selects a collection means with a light load. The collection unit can also select a means for collecting detailed information when the network equipment is in a low-load state. For example, the collection unit selects a means for collecting detailed information when the load of the network equipment is low. The collection unit can also monitor the operating status of the network equipment in real time and dynamically select the optimal collection means. For example, the collection unit monitors the operating status of the network equipment in real time and selects the optimal collection means. In this way, by selecting a collection means according to the operating status of the network equipment, the efficiency and accuracy of collection are improved.

[0036] When collecting network information, the collection unit can prioritize collecting highly relevant information by taking geographical location information into consideration. The collection unit, for example, prioritizes collecting highly relevant network information based on geographical location information. For example, the collection unit collects network information for a specific region using GPS data. The collection unit can also prioritize collecting information for a specific region by taking geographical location information into consideration. For example, the collection unit prioritizes collecting network information for a specific city or region. The collection unit can also prioritize collecting information on important network devices based on geographical location information. For example, the collection unit prioritizes collecting information on network devices in a specific facility or building. In this way, highly relevant information can be collected efficiently by taking geographical location information into consideration.

[0037] The collection unit can analyze social media activities and collect related information when collecting network information. For example, the collection unit analyzes the content of posts on social media and collects related network information. For example, the collection unit analyzes social media posts and collects information about the network. The collection unit can also collect related network information based on check-in information on social media. For example, the collection unit analyzes check-in information at a specific location and collects network information for that location. The collection unit can also collect related network information by referring to the activities of friends on social media. For example, the collection unit analyzes the posts and activities of friends and collects related network information. In this way, related information can be efficiently collected by analyzing social media activities.

[0038] When collecting network information, the collection unit can customize the collection method by reflecting past feedback. The collection unit, for example, optimizes the collection method based on past feedback. For example, the collection unit analyzes past feedback and identifies areas for improvement in the collection method. The collection unit can also customize the collection method by reflecting past feedback. For example, the collection unit adjusts the collection method based on specific feedback. The collection unit can also find areas for improvement in the collection method and optimize it. For example, the collection unit analyzes past feedback and identifies areas for improvement in the collection method. In this way, the collection method can be optimized by reflecting past feedback.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the network information during analysis. For example, the analysis unit performs a detailed analysis on network information with high importance. For example, the analysis unit evaluates the importance of the network information and performs a detailed analysis on the information with high importance. The analysis unit can also perform a simplified analysis on network information with low importance. For example, the analysis unit performs a simplified analysis on information with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis based on the importance of the network information. For example, the analysis unit evaluates the importance of the network information in real time and adjusts the level of detail of the analysis. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the network information.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the network equipment. The analysis unit applies the optimal analysis algorithm depending on the category of the network equipment, such as a router or a switch. For example, the analysis unit selects an appropriate analysis algorithm based on the category of a specific network equipment. The analysis unit can also apply different analysis algorithms depending on the category, such as wireless equipment or wired equipment. For example, the analysis unit applies a specific analysis algorithm to wireless equipment. The analysis unit can also select and apply the optimal analysis algorithm based on the category of the network equipment. For example, the analysis unit selects the optimal analysis algorithm based on the category of the network equipment. In this way, the accuracy of the analysis is improved by applying the optimal analysis algorithm depending on the category of the network equipment.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on past analysis results. For example, the analysis unit analyzes past analysis results and identifies areas for improvement in the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit improves the accuracy of the analysis based on past analysis results. The analysis unit can also analyze past analysis results and find areas for improvement in the analysis to improve the accuracy. For example, the analysis unit analyzes past analysis results and identifies areas for improvement in the analysis. In this way, the accuracy of the analysis is improved by referring to past analysis results.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time when the network information was collected. The analysis unit, for example, prioritizes analyzing the latest network information. For example, the analysis unit prioritizes analyzing the latest information based on the time when the network information was collected. The analysis unit can also prioritize analyzing the latest information over older network information. For example, the analysis unit prioritizes analyzing the latest information over older information. The analysis unit can also dynamically determine the priority of analysis based on the time when the network information was collected. For example, the analysis unit evaluates the time when the network information was collected in real time and determines the priority of analysis. In this way, by determining the priority of analysis based on the time when the network information was collected, the latest information can be analyzed preferentially.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the network information. For example, the analysis unit prioritizes analyzing highly relevant network information. For example, the analysis unit evaluates the relevance of the network information and prioritizes analyzing highly relevant information. The analysis unit can also prioritize important information over less relevant network information. For example, the analysis unit prioritizes analyzing important information over less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the network information. For example, the analysis unit evaluates the relevance of the network information in real time and adjusts the order of analysis. As a result, adjusting the order of analysis based on the relevance of the network information enables efficient analysis.

[0044] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology. For example, the analysis unit evaluates the user's level of expertise and uses detailed technical terminology if the user has technical expertise. The analysis unit can also provide analysis results in simple language if the user does not have technical expertise. For example, the analysis unit provides analysis results in simple language to a user who does not have technical expertise. The analysis unit can also adjust the way the analysis results are presented based on the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise in real time and adjusts the way the analysis results are presented. In this way, analysis results that are easy for the user to understand can be provided by providing analysis results according to the user's level of expertise.

[0045] The generation unit can adjust the level of detail of the NW map based on the importance of the NW information during generation. The generation unit, for example, generates a detailed NW map for NW information with high importance. For example, the generation unit evaluates the importance of the NW information and generates a detailed NW map for information with high importance. The generation unit can also generate a simplified NW map for NW information with low importance. For example, the generation unit generates a simplified NW map for information with low importance. The generation unit can also dynamically adjust the level of detail of the NW map according to the importance of the NW information. For example, the generation unit evaluates the importance of the NW information in real time and adjusts the level of detail of the NW map. This enables efficient NW map generation by adjusting the level of detail of the NW map according to the importance of the NW information.

[0046] The generator can apply different generation algorithms depending on the category of the network equipment during generation. The generator applies the optimal generation algorithm depending on the category of the network equipment, such as a router or a switch. For example, the generator selects an appropriate generation algorithm based on the category of a specific network equipment. The generator can also apply different generation algorithms depending on the category, such as a wireless device or a wired device. For example, the generator applies a specific generation algorithm to wireless devices. The generator can also select and apply the optimal generation algorithm based on the category of the network equipment. For example, the generator selects the optimal generation algorithm based on the category of the network equipment. In this way, the accuracy of the network map is improved by applying the optimal generation algorithm depending on the category of the network equipment.

[0047] The generation unit can improve the accuracy of the NW map by referring to past generation results when generating the map. The generation unit, for example, optimizes the generation algorithm based on past generation results. For example, the generation unit analyzes past generation results and identifies areas for improvement in the generation algorithm. The generation unit can also improve the accuracy of the NW map by referring to past generation results. For example, the generation unit improves the accuracy of the NW map based on past generation results. The generation unit can also analyze past generation results and find areas for improvement in the generation to improve the accuracy. For example, the generation unit analyzes past generation results and identifies areas for improvement in the generation. In this way, the accuracy of the NW map is improved by referring to past generation results.

[0048] The generation unit can determine the priority of the NW map based on the time when the NW information was collected at the time of generation. The generation unit, for example, generates the NW map preferentially based on the latest NW information. For example, the generation unit reflects the latest information preferentially in the NW map based on the time when the NW information was collected. The generation unit can also prioritize the latest information over older NW information. For example, the generation unit reflects the latest information preferentially in the NW map over older information. The generation unit can also dynamically determine the priority of the NW map based on the time when the NW information was collected. For example, the generation unit evaluates the time when the NW information was collected in real time and determines the priority of the NW map. In this way, by determining the priority of the NW map based on the time when the NW information was collected, the latest information can be reflected preferentially in the NW map.

[0049] The generation unit can adjust the order of the NW map based on the relevance of the NW information when generating the map. The generation unit, for example, preferentially reflects highly relevant NW information in the NW map. For example, the generation unit evaluates the relevance of the NW information and preferentially reflects highly relevant information in the NW map. The generation unit can also prioritize important information over less relevant NW information. For example, the generation unit preferentially reflects important information over less relevant information in the NW map. The generation unit can also dynamically adjust the order of the NW map based on the relevance of the NW information. For example, the generation unit evaluates the relevance of the NW information in real time and adjusts the order of the NW map. As a result, the order of the NW map can be adjusted based on the relevance of the NW information, thereby enabling efficient NW map generation.

[0050] The generation unit can adjust the use of technical terms in the network map according to the user's level of expertise when generating the map. For example, if the user has technical expertise, the generation unit uses detailed technical terms. For example, the generation unit evaluates the user's level of expertise and uses detailed technical terms if the user has technical expertise. The generation unit can also generate a network map in simple language if the user does not have technical expertise. For example, the generation unit generates a network map in simple language for a user who does not have technical expertise. The generation unit can also adjust the way the network map is expressed based on the user's level of expertise. For example, the generation unit evaluates the user's level of expertise in real time and adjusts the way the network map is expressed. This makes it possible to provide a network map that is easy for the user to understand by providing a network map according to the user's level of expertise.

[0051] The providing unit can adjust the level of detail of the provided network map based on the importance of the network information when providing the network map. For example, the providing unit provides a detailed network map for network information with high importance. For example, the providing unit evaluates the importance of the network information and provides a detailed network map for information with high importance. The providing unit can also provide a simplified network map for network information with low importance. For example, the providing unit provides a simplified network map for information with low importance. The providing unit can also dynamically adjust the level of detail of the provided network map according to the importance of the network information. For example, the providing unit evaluates the importance of the network information in real time and adjusts the level of detail of the provided network map. This enables efficient provision of the network map by adjusting the level of detail of the provided network map according to the importance of the network information.

[0052] The providing unit can apply different providing algorithms depending on the category of the network equipment when providing the map. The providing unit applies the optimal providing algorithm depending on the category of the network equipment, such as a router or a switch. For example, the providing unit selects an appropriate providing algorithm based on the category of a specific network equipment. The providing unit can also apply different providing algorithms depending on the category, such as a wireless device or a wired device. For example, the providing unit applies a specific providing algorithm to wireless devices. The providing unit can also select and apply the optimal providing algorithm based on the category of the network equipment. For example, the providing unit selects the optimal providing algorithm based on the category of the network equipment. This improves the accuracy of the network map provision by applying the optimal providing algorithm depending on the category of the network equipment.

[0053] When providing the network map, the providing unit can improve the accuracy of providing the network map by referring to past providing results. The providing unit, for example, optimizes the providing algorithm based on past providing results. For example, the providing unit analyzes past providing results and identifies areas for improvement in the providing algorithm. The providing unit can also improve the accuracy of providing the network map by referring to past providing results. For example, the providing unit improves the accuracy of providing the network map based on past providing results. The providing unit can also analyze past providing results and find areas for improvement in providing, thereby improving the accuracy. For example, the providing unit analyzes past providing results and identifies areas for improvement in providing. In this way, the accuracy of providing the network map is improved by referring to past providing results.

[0054] The providing unit can determine the priority of providing the NW map based on the time when the NW information was collected when providing the map. The providing unit, for example, provides the NW map preferentially based on the latest NW information. For example, the providing unit provides the latest information preferentially based on the time when the NW information was collected. The providing unit can also prioritize the latest information over older NW information. For example, the providing unit prioritizes the latest information over older information. The providing unit can also dynamically determine the priority of providing the NW map based on the time when the NW information was collected. For example, the providing unit evaluates the time when the NW information was collected in real time and determines the priority of providing the NW map. In this way, by determining the priority of providing the NW map based on the time when the NW information was collected, the latest information can be provided preferentially.

[0055] The providing unit can adjust the order in which the NW maps are provided based on the relevance of the NW information when providing the maps. For example, the providing unit preferentially reflects highly relevant NW information in the NW map. For example, the providing unit evaluates the relevance of the NW information and preferentially reflects highly relevant information in the NW map. The providing unit can also prioritize important information over less relevant NW information. For example, the providing unit preferentially reflects important information over less relevant information in the NW map. The providing unit can also dynamically adjust the order in which the NW maps are provided based on the relevance of the NW information. For example, the providing unit evaluates the relevance of the NW information in real time and adjusts the order in which the NW maps are provided. As a result, efficient information provision is possible by adjusting the order in which the NW maps are provided based on the relevance of the NW information.

[0056] The providing unit can customize the content of the network map provided according to the user's level of expertise when providing the map. For example, if the user has specialized knowledge, the providing unit uses detailed technical terms. For example, the providing unit evaluates the user's level of expertise and uses detailed technical terms if the user has specialized knowledge. The providing unit can also provide the network map in simple language if the user does not have specialized knowledge. For example, the providing unit provides the network map in simple language to a user who does not have specialized knowledge. The providing unit can also adjust the content of the network map provided based on the user's level of expertise. For example, the providing unit evaluates the user's level of expertise in real time and adjusts the content of the network map provided. In this way, the network map can be provided according to the user's level of expertise, making it possible to provide information that is easy for the user to understand.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The collection unit can analyze past network information collection history and select an appropriate collection method. For example, the collection unit analyzes past collection history and selects the most efficient collection method. For example, the collection unit selects the optimal collection method for a specific time period from the past collection history. The collection unit can also find areas for improvement in the collection method and optimize it. For example, the collection unit analyzes past collection history and identifies areas for improvement in the collection method. The collection unit can also select an appropriate collection method based on the collection means and collection process. For example, the collection unit efficiently collects network information using a specific collection means. In this way, collection efficiency is improved by selecting the optimal collection method based on the past collection history.

[0059] When collecting network information, the collection unit can filter the information based on the type and version of the network equipment. For example, the collection unit filters and collects only the necessary information based on the type of network equipment. For example, the collection unit collects related information based on the type of specific network equipment. The collection unit can also preferentially collect the latest information based on the version of the network equipment. For example, the collection unit collects information based on the latest version of the network equipment. The collection unit can also filter and collect optimal information by combining the type and version of the network equipment. For example, the collection unit collects information based on the type and version of the specific network equipment. In this way, filtering based on the type and version of the network equipment can efficiently collect only the necessary information.

[0060] When collecting network information, the collection unit can select an appropriate collection means according to the operating status of the network equipment. For example, when the network equipment is in a high-load state, the collection unit selects a collection means that reduces the load. For example, the collection unit monitors the load of the network equipment, and when the load is high, selects a collection means that reduces the load. The collection unit can also select a means for collecting detailed information when the network equipment is in a low-load state. For example, the collection unit selects a means for collecting detailed information when the load of the network equipment is low. The collection unit can also monitor the operating status of the network equipment in real time and dynamically select the optimal collection means. For example, the collection unit monitors the operating status of the network equipment in real time and selects the optimal collection means. In this way, by selecting a collection means according to the operating status of the network equipment, the efficiency and accuracy of collection are improved.

[0061] When collecting network information, the collection unit can prioritize collecting highly relevant information by taking geographical location information into consideration. For example, the collection unit prioritizes collecting highly relevant network information based on geographical location information. For example, the collection unit collects network information for a specific region using GPS data. The collection unit can also prioritize collecting information for a specific region by taking geographical location information into consideration. For example, the collection unit prioritizes collecting network information for a specific city or region. The collection unit can also prioritize collecting information about important network devices based on geographical location information. For example, the collection unit prioritizes collecting information about network devices in a specific facility or building. In this way, highly relevant information can be collected efficiently by taking geographical location information into consideration.

[0062] When collecting network information, the collection unit can analyze social media activities and collect related information. For example, the collection unit analyzes the content of posts on social media and collects related network information. For example, the collection unit analyzes social media posts and collects information about the network. The collection unit can also collect related network information based on check-in information on social media. For example, the collection unit analyzes check-in information at a specific location and collects network information for that location. The collection unit can also collect related network information by referring to the activities of friends on social media. For example, the collection unit analyzes the posts and activities of friends and collects related network information. In this way, related information can be efficiently collected by analyzing social media activities.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects network information obtained from network devices. The collection unit collects, for example, network traffic data, connection logs, and configuration information. It can also collect network information based on the collection frequency and selection criteria for the items to be collected. Step 2: The analysis unit understands and analyzes the connection rules based on the network information collected by the collection unit. The analysis unit uses, for example, LLM to understand advanced connection rules and analyze the collected network information. It can also perform analysis based on the algorithm used and the purpose of the analysis. Step 3: The generator generates a network map using natural language processing based on the results of the analysis by the analyzer. The generator generates the network map using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generator can also generate the network map in the form of a topology map, connectivity map, or other format. Step 4: The providing unit immediately provides the network map generated by the generating unit. The providing unit provides the network map based on, for example, a delay time or a providing means. The network map can also be provided through a web application or a mobile application.

[0065] (Example 2) A system according to an embodiment of the present invention aims to improve the efficiency of network design and maintenance in the telecommunications industry. This system collects network information obtained from network equipment, uses a large-scale language model (LLM) to understand and analyze advanced connection rules, and generates and provides a network map using natural language processing in real time. This allows the system to provide the latest network map in real time, thereby improving the efficiency of network design and maintenance. For example, the system collects network information obtained from network equipment, and the LLM analyzes it after understanding the advanced connection rules. Next, the LLM generates a network map using natural language processing, which is updated in real time. This system is used in network design and maintenance. Furthermore, the completion of an LLM specialized for the network domain enables automatic fault resolution. For example, this system enables automatic operation when a fault occurs, detection and automatic correction of network information configuration errors, and support for network design and maintenance through question-and-answer sessions with the LLM.

[0066] A network design and maintenance system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects network information obtained from network devices. The collection unit collects, for example, network traffic data, connection logs, and configuration information from the network devices. The collection unit can also collect network information based on collection frequency and selection criteria for collection targets. The analysis unit understands and analyzes connection rules based on the network information collected by the collection unit. The analysis unit understands advanced connection rules using, for example, LLM and analyzes the collected network information. The analysis unit can also perform analysis based on the algorithm used and the purpose of the analysis. The generation unit generates a network map using natural language processing based on the results of the analysis by the analysis unit. The generation unit generates the network map using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generation unit can also generate the network map in the form of a topology map, a connectivity map, or the like. The provision unit immediately provides the network map generated by the generation unit. The providing unit provides the network map based on, for example, the delay time and the means of providing. The providing unit can also provide the network map through a web application or a mobile application. As a result, the network design and maintenance system according to the embodiment can provide the latest network map in real time by collecting, analyzing, generating, and providing information obtained from network devices.

[0067] The analysis unit can perform automatic operation when a failure occurs. The analysis unit performs automatic operation depending on the type of failure, for example. For example, the analysis unit detects a network failure and automatically restarts or resets the system. The analysis unit can also identify the cause of the failure and execute appropriate response procedures. For example, the analysis unit can identify the location of the failure and change the settings of the relevant network equipment. The analysis unit can also evaluate the extent of the failure's impact and take measures to minimize the impact. For example, the analysis unit can set up a route to bypass the network equipment affected by the failure. This enables rapid response to the failure by automatically responding when a failure occurs.

[0068] The analysis unit can detect configuration errors in network information and perform automatic corrections. For example, the analysis unit detects erroneous configuration items and automatically corrects them. For example, the analysis unit analyzes configuration information of network devices and identifies erroneous settings. The analysis unit can also detect configuration errors based on an error detection method. For example, the analysis unit checks the consistency of configuration information and detects inconsistent settings. The analysis unit can also perform automatic corrections based on a correction procedure. For example, the analysis unit corrects erroneous settings and changes them to correct settings. The analysis unit can also correct configuration errors based on the scope of application of the correction. For example, the analysis unit corrects configuration errors in specific network devices. This automatically corrects configuration errors in network information, thereby improving the stability of the network.

[0069] The provisioning unit can provide support in the area of ​​network design and maintenance through question and answer sessions with the LLM. For example, the provisioning unit uses the LLM to provide answers to questions about network design and maintenance. For example, the provisioning unit inputs a question such as "Please tell me how to configure this network device" into the LLM, and the LLM generates an appropriate answer. The provisioning unit can also provide support based on the question and answer process. For example, the provisioning unit provides information about network design and maintenance through dialogue with the LLM. The provisioning unit can also conduct question and answer sessions based on the type of LLM used. For example, the provisioning unit answers questions about network design and maintenance using a specific LLM. This improves the efficiency of network design and maintenance through question and answer sessions with the LLM.

[0070] The collection unit can estimate the user's emotions and adjust the timing of collecting network information based on the estimated user emotions. The collection unit, for example, uses an emotion estimation algorithm to estimate the user's emotions. For example, the collection unit analyzes the user's facial expressions and voice and calculates an emotion score. The collection unit can also adjust the collection timing based on the type of emotion. For example, if the user is feeling stressed, the collection unit delays the collection timing to reduce the burden on the user. Also, if the user is relaxed, the collection unit can accelerate the collection timing to efficiently collect information. For example, if the user is in a hurry, the collection unit immediately collects network information to enable a prompt response. This reduces the burden on the user by adjusting the collection timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The collection unit can analyze past network information collection history and select an appropriate collection method. For example, the collection unit analyzes past collection history and selects the most efficient collection method. For example, the collection unit selects the optimal collection method for a specific time period from the past collection history. The collection unit can also find areas for improvement in the collection method and optimize it. For example, the collection unit analyzes past collection history and identifies areas for improvement in the collection method. The collection unit can also select an appropriate collection method based on the collection means and collection process. For example, the collection unit efficiently collects network information using a specific collection means. In this way, collection efficiency is improved by selecting the optimal collection method based on the past collection history.

[0072] When collecting network information, the collection unit can perform filtering based on the type and version of network equipment. For example, the collection unit filters and collects only necessary information based on the type of network equipment. For example, the collection unit collects related information based on the type of specific network equipment. The collection unit can also preferentially collect the latest information based on the version of the network equipment. For example, the collection unit collects information based on the latest version of network equipment. The collection unit can also filter and collect optimal information by combining the type and version of network equipment. For example, the collection unit collects information based on the type and version of specific network equipment. In this way, filtering based on the type and version of network equipment can efficiently collect only necessary information.

[0073] When collecting network information, the collection unit can select an appropriate collection means according to the operating status of the network equipment. For example, when the network equipment is in a high-load state, the collection unit selects a collection means that reduces the load. For example, the collection unit monitors the load of the network equipment, and when the load is high, selects a collection means with a light load. The collection unit can also select a means for collecting detailed information when the network equipment is in a low-load state. For example, the collection unit selects a means for collecting detailed information when the load of the network equipment is low. The collection unit can also monitor the operating status of the network equipment in real time and dynamically select the optimal collection means. For example, the collection unit monitors the operating status of the network equipment in real time and selects the optimal collection means. In this way, by selecting a collection means according to the operating status of the network equipment, the efficiency and accuracy of collection are improved.

[0074] The collection unit can estimate the user's emotions and determine the priority of the network information to be collected based on the estimated user emotions. The collection unit, for example, uses an emotion estimation algorithm to estimate the user's emotions. For example, the collection unit analyzes the user's facial expressions and voice and calculates an emotion score. The collection unit can also determine the priority of the network information to be collected based on the type of emotion. For example, if the user is stressed, the collection unit prioritizes collecting information of high importance. The collection unit can also collect detailed information if the user is relaxed. For example, if the user is in a hurry, the collection unit prioritizes information that can be collected quickly. This enables efficient information collection by determining the priority of the information to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When collecting network information, the collection unit can prioritize collecting highly relevant information by taking geographical location information into consideration. The collection unit, for example, prioritizes collecting highly relevant network information based on geographical location information. For example, the collection unit collects network information for a specific region using GPS data. The collection unit can also prioritize collecting information for a specific region by taking geographical location information into consideration. For example, the collection unit prioritizes collecting network information for a specific city or region. The collection unit can also prioritize collecting information on important network devices based on geographical location information. For example, the collection unit prioritizes collecting information on network devices in a specific facility or building. In this way, highly relevant information can be collected efficiently by taking geographical location information into consideration.

[0076] The collection unit can analyze social media activities and collect related information when collecting network information. For example, the collection unit analyzes the content of posts on social media and collects related network information. For example, the collection unit analyzes social media posts and collects information about the network. The collection unit can also collect related network information based on check-in information on social media. For example, the collection unit analyzes check-in information at a specific location and collects network information for that location. The collection unit can also collect related network information by referring to the activities of friends on social media. For example, the collection unit analyzes the posts and activities of friends and collects related network information. In this way, related information can be efficiently collected by analyzing social media activities.

[0077] When collecting network information, the collection unit can customize the collection method by reflecting past feedback. The collection unit, for example, optimizes the collection method based on past feedback. For example, the collection unit analyzes past feedback and identifies areas for improvement in the collection method. The collection unit can also customize the collection method by reflecting past feedback. For example, the collection unit adjusts the collection method based on specific feedback. The collection unit can also find areas for improvement in the collection method and optimize it. For example, the collection unit analyzes past feedback and identifies areas for improvement in the collection method. In this way, the collection method can be optimized by reflecting past feedback.

[0078] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit can also adjust the presentation method of the analysis based on the type of emotion. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is in a hurry, the analysis unit provides a summary analysis result. This allows the analysis result to be easily understood by adjusting the presentation method of the analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the network information during analysis. For example, the analysis unit performs a detailed analysis on network information with high importance. For example, the analysis unit evaluates the importance of the network information and performs a detailed analysis on the information with high importance. The analysis unit can also perform a simplified analysis on network information with low importance. For example, the analysis unit performs a simplified analysis on information with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis based on the importance of the network information. For example, the analysis unit evaluates the importance of the network information in real time and adjusts the level of detail of the analysis. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the network information.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the network equipment. The analysis unit applies the optimal analysis algorithm depending on the category of the network equipment, such as a router or a switch. For example, the analysis unit selects an appropriate analysis algorithm based on the category of a specific network equipment. The analysis unit can also apply different analysis algorithms depending on the category, such as wireless equipment or wired equipment. For example, the analysis unit applies a specific analysis algorithm to wireless equipment. The analysis unit can also select and apply the optimal analysis algorithm based on the category of the network equipment. For example, the analysis unit selects the optimal analysis algorithm based on the category of the network equipment. In this way, the accuracy of the analysis is improved by applying the optimal analysis algorithm depending on the category of the network equipment.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on past analysis results. For example, the analysis unit analyzes past analysis results and identifies areas for improvement in the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit improves the accuracy of the analysis based on past analysis results. The analysis unit can also analyze past analysis results and find areas for improvement in the analysis to improve the accuracy. For example, the analysis unit analyzes past analysis results and identifies areas for improvement in the analysis. In this way, the accuracy of the analysis is improved by referring to past analysis results.

[0082] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. The analysis unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit can also adjust the length of the analysis based on the type of emotion. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows the analysis length to be adjusted according to the user's emotion, thereby providing the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] During analysis, the analysis unit can determine the priority of analysis based on the time when the network information was collected. The analysis unit, for example, prioritizes analyzing the latest network information. For example, the analysis unit prioritizes analyzing the latest information based on the time when the network information was collected. The analysis unit can also prioritize analyzing the latest information over older network information. For example, the analysis unit prioritizes analyzing the latest information over older information. The analysis unit can also dynamically determine the priority of analysis based on the time when the network information was collected. For example, the analysis unit evaluates the time when the network information was collected in real time and determines the priority of analysis. In this way, by determining the priority of analysis based on the time when the network information was collected, the latest information can be analyzed preferentially.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the network information. For example, the analysis unit prioritizes analyzing highly relevant network information. For example, the analysis unit evaluates the relevance of the network information and prioritizes analyzing highly relevant information. The analysis unit can also prioritize important information over less relevant network information. For example, the analysis unit prioritizes analyzing important information over less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the network information. For example, the analysis unit evaluates the relevance of the network information in real time and adjusts the order of analysis. As a result, adjusting the order of analysis based on the relevance of the network information enables efficient analysis.

[0085] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology. For example, the analysis unit evaluates the user's level of expertise and uses detailed technical terminology if the user has technical expertise. The analysis unit can also provide analysis results in simple language if the user does not have technical expertise. For example, the analysis unit provides analysis results in simple language to a user who does not have technical expertise. The analysis unit can also adjust the way the analysis results are presented based on the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise in real time and adjusts the way the analysis results are presented. In this way, analysis results that are easy for the user to understand can be provided by providing analysis results according to the user's level of expertise.

[0086] The generation unit can estimate the user's emotion and adjust the method for generating the network map based on the estimated user emotion. The generation unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. The generation unit can also adjust the method for generating the network map based on the type of emotion. For example, if the user is relaxed, the generation unit generates a network map that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate a network map that emphasizes the shortest route. For example, if the user is excited, the generation unit generates a network map that adds a visually stimulating effect. In this way, by adjusting the method for generating the network map according to the user's emotion, it is possible to provide an optimal network map for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The generation unit can adjust the level of detail of the NW map based on the importance of the NW information during generation. The generation unit, for example, generates a detailed NW map for NW information with high importance. For example, the generation unit evaluates the importance of the NW information and generates a detailed NW map for information with high importance. The generation unit can also generate a simplified NW map for NW information with low importance. For example, the generation unit generates a simplified NW map for information with low importance. The generation unit can also dynamically adjust the level of detail of the NW map according to the importance of the NW information. For example, the generation unit evaluates the importance of the NW information in real time and adjusts the level of detail of the NW map. This enables efficient NW map generation by adjusting the level of detail of the NW map according to the importance of the NW information.

[0088] The generator can apply different generation algorithms depending on the category of the network equipment during generation. The generator applies the optimal generation algorithm depending on the category of the network equipment, such as a router or a switch. For example, the generator selects an appropriate generation algorithm based on the category of a specific network equipment. The generator can also apply different generation algorithms depending on the category, such as a wireless device or a wired device. For example, the generator applies a specific generation algorithm to wireless devices. The generator can also select and apply the optimal generation algorithm based on the category of the network equipment. For example, the generator selects the optimal generation algorithm based on the category of the network equipment. In this way, the accuracy of the network map is improved by applying the optimal generation algorithm depending on the category of the network equipment.

[0089] The generation unit can improve the accuracy of the NW map by referring to past generation results when generating the map. The generation unit, for example, optimizes the generation algorithm based on past generation results. For example, the generation unit analyzes past generation results and identifies areas for improvement in the generation algorithm. The generation unit can also improve the accuracy of the NW map by referring to past generation results. For example, the generation unit improves the accuracy of the NW map based on past generation results. The generation unit can also analyze past generation results and find areas for improvement in the generation to improve the accuracy. For example, the generation unit analyzes past generation results and identifies areas for improvement in the generation. In this way, the accuracy of the NW map is improved by referring to past generation results.

[0090] The generation unit can estimate the user's emotion and adjust the length of the NW map based on the estimated user emotion. The generation unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. The generation unit can also adjust the length of the NW map based on the type of emotion. For example, if the user is in a hurry, the generation unit generates a short and to-the-point NW map. If the user is relaxed, the generation unit can generate a longer NW map with detailed explanations. For example, if the user is excited, the generation unit generates a NW map with a visually stimulating effect. This allows the length of the NW map to be adjusted according to the user's emotion, thereby providing an optimal NW map for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The generation unit can determine the priority of the NW map based on the time when the NW information was collected at the time of generation. The generation unit, for example, generates the NW map preferentially based on the latest NW information. For example, the generation unit reflects the latest information preferentially in the NW map based on the time when the NW information was collected. The generation unit can also prioritize the latest information over older NW information. For example, the generation unit reflects the latest information preferentially in the NW map over older information. The generation unit can also dynamically determine the priority of the NW map based on the time when the NW information was collected. For example, the generation unit evaluates the time when the NW information was collected in real time and determines the priority of the NW map. In this way, by determining the priority of the NW map based on the time when the NW information was collected, the latest information can be reflected preferentially in the NW map.

[0092] The generation unit can adjust the order of the NW map based on the relevance of the NW information when generating the map. The generation unit, for example, preferentially reflects highly relevant NW information in the NW map. For example, the generation unit evaluates the relevance of the NW information and preferentially reflects highly relevant information in the NW map. The generation unit can also prioritize important information over less relevant NW information. For example, the generation unit preferentially reflects important information over less relevant information in the NW map. The generation unit can also dynamically adjust the order of the NW map based on the relevance of the NW information. For example, the generation unit evaluates the relevance of the NW information in real time and adjusts the order of the NW map. As a result, the order of the NW map can be adjusted based on the relevance of the NW information, thereby enabling efficient NW map generation.

[0093] The generation unit can adjust the use of technical terms in the network map according to the user's level of expertise when generating the map. For example, if the user has technical expertise, the generation unit uses detailed technical terms. For example, the generation unit evaluates the user's level of expertise and uses detailed technical terms if the user has technical expertise. The generation unit can also generate a network map in simple language if the user does not have technical expertise. For example, the generation unit generates a network map in simple language for a user who does not have technical expertise. The generation unit can also adjust the way the network map is expressed based on the user's level of expertise. For example, the generation unit evaluates the user's level of expertise in real time and adjusts the way the network map is expressed. This makes it possible to provide a network map that is easy for the user to understand by providing a network map according to the user's level of expertise.

[0094] The providing unit can estimate the user's emotion and adjust the method of providing the NW map based on the estimated user emotion. The providing unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the providing unit analyzes the user's facial expressions and voice and calculates an emotion score. The providing unit can also adjust the method of providing the NW map based on the type of emotion. For example, if the user is nervous, the providing unit provides a simple and highly visible method of providing the map. Also, if the user is relaxed, the providing unit can provide a method of providing the map that includes detailed information. For example, if the user is in a hurry, the providing unit provides a method of providing the map that focuses on the main points. In this way, by adjusting the method of providing the NW map according to the user's emotion, it is possible to provide the optimal method of providing the map to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The providing unit can adjust the level of detail of the provided network map based on the importance of the network information when providing the network map. For example, the providing unit provides a detailed network map for network information with high importance. For example, the providing unit evaluates the importance of the network information and provides a detailed network map for information with high importance. The providing unit can also provide a simplified network map for network information with low importance. For example, the providing unit provides a simplified network map for information with low importance. The providing unit can also dynamically adjust the level of detail of the provided network map according to the importance of the network information. For example, the providing unit evaluates the importance of the network information in real time and adjusts the level of detail of the provided network map. This enables efficient provision of the network map by adjusting the level of detail of the provided network map according to the importance of the network information.

[0096] The providing unit can apply different providing algorithms depending on the category of the network equipment when providing the map. The providing unit applies the optimal providing algorithm depending on the category of the network equipment, such as a router or a switch. For example, the providing unit selects an appropriate providing algorithm based on the category of a specific network equipment. The providing unit can also apply different providing algorithms depending on the category, such as a wireless device or a wired device. For example, the providing unit applies a specific providing algorithm to wireless devices. The providing unit can also select and apply the optimal providing algorithm based on the category of the network equipment. For example, the providing unit selects the optimal providing algorithm based on the category of the network equipment. This improves the accuracy of the network map provision by applying the optimal providing algorithm depending on the category of the network equipment.

[0097] When providing the network map, the providing unit can improve the accuracy of providing the network map by referring to past providing results. The providing unit, for example, optimizes the providing algorithm based on past providing results. For example, the providing unit analyzes past providing results and identifies areas for improvement in the providing algorithm. The providing unit can also improve the accuracy of providing the network map by referring to past providing results. For example, the providing unit improves the accuracy of providing the network map based on past providing results. The providing unit can also analyze past providing results and find areas for improvement in providing, thereby improving the accuracy. For example, the providing unit analyzes past providing results and identifies areas for improvement in providing. In this way, the accuracy of providing the network map is improved by referring to past providing results.

[0098] The providing unit can estimate the user's emotion and adjust the order in which the NW maps are provided based on the estimated user emotion. The providing unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the providing unit analyzes the user's facial expressions and voice and calculates an emotion score. The providing unit can also adjust the order in which the NW maps are provided based on the type of emotion. For example, if the user is nervous, the providing unit prioritizes providing important information. The providing unit can also provide detailed information if the user is relaxed. For example, if the user is in a hurry, the providing unit prioritizes providing information that covers the main points. In this way, by adjusting the order in which the NW maps are provided according to the user's emotion, information can be provided in an order that is optimal for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The providing unit can determine the priority of providing the NW map based on the time when the NW information was collected when providing the map. The providing unit, for example, provides the NW map preferentially based on the latest NW information. For example, the providing unit provides the latest information preferentially based on the time when the NW information was collected. The providing unit can also prioritize the latest information over older NW information. For example, the providing unit prioritizes the latest information over older information. The providing unit can also dynamically determine the priority of providing the NW map based on the time when the NW information was collected. For example, the providing unit evaluates the time when the NW information was collected in real time and determines the priority of providing the NW map. In this way, by determining the priority of providing the NW map based on the time when the NW information was collected, the latest information can be provided preferentially.

[0100] The providing unit can adjust the order in which the NW maps are provided based on the relevance of the NW information when providing the maps. For example, the providing unit preferentially reflects highly relevant NW information in the NW map. For example, the providing unit evaluates the relevance of the NW information and preferentially reflects highly relevant information in the NW map. The providing unit can also prioritize important information over less relevant NW information. For example, the providing unit preferentially reflects important information over less relevant information in the NW map. The providing unit can also dynamically adjust the order in which the NW maps are provided based on the relevance of the NW information. For example, the providing unit evaluates the relevance of the NW information in real time and adjusts the order in which the NW maps are provided. As a result, efficient information provision is possible by adjusting the order in which the NW maps are provided based on the relevance of the NW information.

[0101] The providing unit can customize the content of the network map provided according to the user's level of expertise when providing the map. For example, if the user has specialized knowledge, the providing unit uses detailed technical terms. For example, the providing unit evaluates the user's level of expertise and uses detailed technical terms if the user has specialized knowledge. The providing unit can also provide the network map in simple language if the user does not have specialized knowledge. For example, the providing unit provides the network map in simple language to a user who does not have specialized knowledge. The providing unit can also adjust the content of the network map provided based on the user's level of expertise. For example, the providing unit evaluates the user's level of expertise in real time and adjusts the content of the network map provided. In this way, the network map can be provided according to the user's level of expertise, making it possible to provide information that is easy for the user to understand. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects NW information obtained from NW devices using the camera 42 and microphone 38B of the smart device 14, and executes the collection process using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses LLM to understand advanced connection rules and analyze the collected NW information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a NW map using natural language processing. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated NW map in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects NW information obtained from NW devices using the camera 42 and microphone 238 of the smart glasses 214, and executes the collection process using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses LLM to understand advanced connection rules and analyze the collected NW information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a NW map using natural language processing. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated NW map in real time. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects NW information obtained from NW devices using the camera 42 and microphone 238 of the headset type terminal 314, and executes the collection process using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and uses LLM to understand advanced connection rules and analyze the collected NW information. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and generates a NW map using natural language processing. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated NW map in real time. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects NW information obtained from NW devices using the camera 42 and microphone 238 of the robot 414, and the control unit 46A executes the collection process. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses LLM to understand advanced connection rules and analyze the collected NW information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a NW map using natural language processing. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated NW map in real time.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The collection unit can estimate the user's emotions and determine the priority of network information to be collected based on the estimated user emotions. For example, the collection unit analyzes the user's facial expressions and voice and calculates an emotion score. The collection unit can also determine the priority of network information to be collected based on the type of emotion. For example, if the user is feeling stressed, the collection unit prioritizes collecting information of high importance. The collection unit can also collect detailed information if the user is relaxed. For example, if the user is in a hurry, the collection unit prioritizes information that can be collected quickly. This enables efficient information collection by determining the priority of information to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, the analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit can also adjust the way the analysis is presented based on the type of emotion. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is in a hurry, the analysis unit provides analysis results that focus on the main points. This allows the analysis results to be easily understood by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The generation unit can estimate the user's emotions and adjust the method for generating the network map based on the estimated user emotions. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. The generation unit can also adjust the method for generating the network map based on the type of emotion. For example, if the user is relaxed, the generation unit generates a network map that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate a network map that emphasizes the shortest route. For example, if the user is excited, the generation unit generates a network map that adds a visually stimulating effect. In this way, by adjusting the method for generating the network map according to the user's emotions, it is possible to provide an optimal network map for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0106] The providing unit can estimate the user's emotions and adjust the method of providing the NW map based on the estimated user emotions. For example, the providing unit analyzes the user's facial expressions and voice and calculates an emotion score. The providing unit can also adjust the method of providing the NW map based on the type of emotion. For example, if the user is nervous, the providing unit provides a simple and highly visible method of providing the map. If the user is relaxed, the providing unit can also provide a method of providing the map that includes detailed information. For example, if the user is in a hurry, the providing unit provides a method of providing the map that focuses on the main points. This allows the method of providing the NW map to be adjusted according to the user's emotions, thereby providing the optimal method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] The providing unit can estimate the user's emotions and adjust the order in which the NW maps are provided based on the estimated user emotions. For example, the providing unit analyzes the user's facial expressions and voice and calculates an emotion score. The providing unit can also adjust the order in which the NW maps are provided based on the type of emotion. For example, if the user is nervous, the providing unit prioritizes providing important information. The providing unit can also provide detailed information if the user is relaxed. For example, if the user is in a hurry, the providing unit prioritizes providing information that covers the main points. In this way, by adjusting the order in which the NW maps are provided according to the user's emotions, information can be provided in an order that is optimal for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0108] The collection unit can analyze past network information collection history and select an appropriate collection method. For example, the collection unit analyzes past collection history and selects the most efficient collection method. For example, the collection unit selects the optimal collection method for a specific time period from the past collection history. The collection unit can also find areas for improvement in the collection method and optimize it. For example, the collection unit analyzes past collection history and identifies areas for improvement in the collection method. The collection unit can also select an appropriate collection method based on the collection means and collection process. For example, the collection unit efficiently collects network information using a specific collection means. In this way, collection efficiency is improved by selecting the optimal collection method based on the past collection history.

[0109] When collecting network information, the collection unit can filter the information based on the type and version of the network equipment. For example, the collection unit filters and collects only the necessary information based on the type of network equipment. For example, the collection unit collects related information based on the type of specific network equipment. The collection unit can also preferentially collect the latest information based on the version of the network equipment. For example, the collection unit collects information based on the latest version of the network equipment. The collection unit can also filter and collect optimal information by combining the type and version of the network equipment. For example, the collection unit collects information based on the type and version of the specific network equipment. In this way, filtering based on the type and version of the network equipment can efficiently collect only the necessary information.

[0110] When collecting network information, the collection unit can select an appropriate collection means according to the operating status of the network equipment. For example, when the network equipment is in a high-load state, the collection unit selects a collection means that reduces the load. For example, the collection unit monitors the load of the network equipment, and when the load is high, selects a collection means that reduces the load. The collection unit can also select a means for collecting detailed information when the network equipment is in a low-load state. For example, the collection unit selects a means for collecting detailed information when the load of the network equipment is low. The collection unit can also monitor the operating status of the network equipment in real time and dynamically select the optimal collection means. For example, the collection unit monitors the operating status of the network equipment in real time and selects the optimal collection means. In this way, by selecting a collection means according to the operating status of the network equipment, the efficiency and accuracy of collection are improved.

[0111] When collecting network information, the collection unit can prioritize collecting highly relevant information by taking geographical location information into consideration. For example, the collection unit prioritizes collecting highly relevant network information based on geographical location information. For example, the collection unit collects network information for a specific region using GPS data. The collection unit can also prioritize collecting information for a specific region by taking geographical location information into consideration. For example, the collection unit prioritizes collecting network information for a specific city or region. The collection unit can also prioritize collecting information about important network devices based on geographical location information. For example, the collection unit prioritizes collecting information about network devices in a specific facility or building. In this way, highly relevant information can be collected efficiently by taking geographical location information into consideration.

[0112] When collecting network information, the collection unit can analyze social media activities and collect related information. For example, the collection unit analyzes the content of posts on social media and collects related network information. For example, the collection unit analyzes social media posts and collects information about the network. The collection unit can also collect related network information based on check-in information on social media. For example, the collection unit analyzes check-in information at a specific location and collects network information for that location. The collection unit can also collect related network information by referring to the activities of friends on social media. For example, the collection unit analyzes the posts and activities of friends and collects related network information. In this way, related information can be efficiently collected by analyzing social media activities.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The collection unit collects network information obtained from network devices. The collection unit collects, for example, network traffic data, connection logs, and configuration information. It can also collect network information based on the collection frequency and selection criteria for the items to be collected. Step 2: The analysis unit understands and analyzes the connection rules based on the network information collected by the collection unit. The analysis unit uses, for example, LLM to understand advanced connection rules and analyze the collected network information. It can also perform analysis based on the algorithm used and the purpose of the analysis. Step 3: The generator generates a network map using natural language processing based on the results of the analysis by the analyzer. The generator generates the network map using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generator can also generate the network map in the form of a topology map, connectivity map, or other format. Step 4: The providing unit immediately provides the network map generated by the generating unit. The providing unit provides the network map based on, for example, a delay time or a providing means. The network map can also be provided through a web application or a mobile application.

[0115] 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.

[0116] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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, in order to avoid confusion and to 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 collection unit that collects network information obtained from network devices; an analysis unit that understands and analyzes connection rules based on the NW information collected by the collection unit; a generation unit that generates a network map using natural language processing based on the results of the analysis by the analysis unit; a providing unit that immediately provides the NW map generated by the generating unit. A system characterized by:

2. The analysis unit Automatic operation when a failure occurs 2. The system of claim 1.

3. The analysis unit Detects and automatically corrects network information configuration errors 2. The system of claim 1.

4. The providing unit Support in the area of ​​network design and maintenance through Q&A with LLMs 2. The system of claim 1.

5. The collecting unit Estimates user emotions and adjusts the timing of network information collection based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit Analyze past network information collection history and select the appropriate collection method 2. The system of claim 1.

7. The collecting unit When collecting network information, filtering is performed based on the type and version of the network equipment.

2. The system of claim 1.

8. The collecting unit When collecting network information, select the appropriate collection method depending on the operating status of the network equipment.

2. The system of claim 1.

9. The collecting unit Estimate user emotions and prioritize network information collection based on the estimated user emotions.

2. The system of claim 1.

10. The collecting unit When collecting network information, prioritize collecting highly relevant information by taking into account geographical location information.

2. The system of claim 1.

Images