system

Abstract

The system according to the embodiment aims to automatically and accurately generate a network configuration diagram for a company. [Solution] A system according to an embodiment includes a reception unit, a data acquisition unit, an analysis unit, a visualization unit, and a provision unit. The reception unit inputs a company number. The data acquisition unit acquires base data from Ebis based on the company number input by the reception unit. The analysis unit analyzes the base data acquired by the data acquisition unit. The visualization unit generates a network configuration diagram based on the data analyzed by the analysis unit. The provision unit provides the network configuration diagram generated by the visualization unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, creating a company's network configuration diagram was a time-consuming and inaccurate process.

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

[0006] The system according to the embodiment includes a reception unit, a data acquisition unit, an analysis unit, a visualization unit, and a provision unit. The reception unit inputs a company number. The data acquisition unit acquires base data from Ebis based on the company number input by the reception unit. The analysis unit analyzes the base data acquired by the data acquisition unit. The visualization unit generates a network configuration diagram based on the data analyzed by the analysis unit. The provision unit provides the network configuration diagram generated by the visualization unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically and accurately generate a network configuration diagram for a company. [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) The enterprise account sales support system according to an embodiment of the present invention automatically generates and provides network configuration diagrams based on enterprise numbers. The system inputs enterprise numbers, retrieves base data from Ebis, and collects information on the location name, line type, bandwidth, and associated managed routers for each closed area. Based on this information, the system automatically generates network configuration diagrams. The generated configuration diagrams are visually easy to understand, allowing users to grasp at a glance the connection status, line type, bandwidth, and managed router location of each location. For example, when creating a network configuration diagram for a company, simply enter the enterprise number. The necessary data is automatically retrieved from Ebis and visualizes the information for each location. This allows sales representatives to quickly create accurate network configuration diagrams, enabling them to provide prompt and accurate proposals to customers. This system prevents manual errors and significantly improves work efficiency, making it a powerful support tool for enterprise account sales. This allows the enterprise account sales support system to automatically generate and provide network configuration diagrams based on enterprise numbers. For example, sales representatives can quickly create accurate network configuration diagrams, enabling them to provide prompt and accurate proposals to customers. It also helps prevent manual errors and significantly improves work efficiency.

[0029] An enterprise account sales support system according to an embodiment includes a reception unit, a data acquisition unit, an analysis unit, a visualization unit, and a provision unit. The reception unit inputs a company number. The company number includes, for example, a company code or a registration number, but is not limited to these examples. The data acquisition unit acquires base data from Ebis based on the company number input by the reception unit. The base data includes, for example, basic company information and network configuration information, but is not limited to these examples. The analysis unit analyzes the acquired base data and organizes information on the location name, line type, bandwidth, and associated managed router for each closed area. The closed area includes, for example, specific network segments and security zones, but is not limited to these examples. The visualization unit generates a network configuration diagram based on the analyzed data. The network configuration diagram includes, for example, a method for displaying nodes and edges, the level of detail of the diagram, and the like, but is not limited to these examples. The provision unit provides the generated network configuration diagram to a user. The provision unit displays the generated network configuration diagram to a user, for example, via a web application or a mobile application. As a result, the enterprise account sales support system according to the embodiment can automatically generate and provide a network configuration diagram based on the enterprise number. For example, sales representatives can create accurate network configuration diagrams in a short amount of time, enabling them to make prompt and accurate proposals to customers. This also prevents manual errors and significantly improves work efficiency.

[0030] The data acquisition unit can acquire base data from Ebis. The data acquisition unit acquires base data such as basic company information and network configuration information from Ebis, for example. Ebis is a database provided by a specific provider, and includes basic company information and network configuration information, for example. By acquiring base data from Ebis, it is possible to secure the information necessary for generating a network configuration diagram. Some or all of the above-described processing in the data acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the data acquisition unit may input the data acquired from Ebis into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit analyzes the acquired base data and organizes information on the location name, line type, bandwidth, and associated managed router for each closed area. The analysis unit, for example, analyzes the acquired base data and organizes information on the location name, line type, bandwidth, and associated managed router for each closed area. Closed areas include, for example, specific network segments and security zones, but are not limited to these examples. Location names include, for example, office names and data center names, but are not limited to these examples. Line types include, for example, optical fiber lines and dedicated lines, but are not limited to these examples. Bandwidths include, for example, units such as Mbps and Gbps, but are not limited to these examples. Managed routers include, for example, managed routers and routers from specific vendors, but are not limited to these examples. By analyzing the base data and organizing the necessary information, the accuracy of the network configuration diagram is improved. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the acquired base data into the generation AI and have the generation AI analyze the data.

[0032] The visualization unit can generate a network configuration diagram based on the analyzed data. The visualization unit generates the network configuration diagram based on, for example, the analyzed data. The network configuration diagram may include, for example, but is not limited to, the method of displaying nodes and edges and the level of detail of the diagram. By generating the network configuration diagram based on the analyzed data, a visually easy-to-understand configuration diagram can be obtained. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit may input the analyzed data to a generation AI and cause the generation AI to generate a network configuration diagram.

[0033] The providing unit can provide the generated network configuration diagram to the user. For example, the providing unit displays the generated network configuration diagram to the user via a web application or a mobile application. By providing the generated network configuration diagram to the user, the user can quickly and accurately grasp the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated network configuration diagram to a generating AI and cause the generating AI to execute an optimal display method for providing the diagram to the user.

[0034] When entering a company number, the reception unit can perform input completion by referring to the user's past input history. For example, the reception unit can automatically display company numbers that the user has frequently entered in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest company numbers to be used during specific time periods based on the user's past input history. By referring to the past input history, this reduces the effort required for input and enables efficient input. Input completion is achieved, for example, by an auto-complete function or a method using past input data. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI perform input completion.

[0035] The reception unit can diversify input methods for the enterprise number by using voice input or image recognition. For example, the reception unit allows the user to simply input the enterprise number by voice, and the system automatically recognizes it. The reception unit can also allow the user to input the enterprise number by hand and automatically convert it into text using image recognition technology. The reception unit can also allow the user to take a photo of a company logo with a camera and automatically obtain the enterprise number using image recognition technology. This allows the user to input the enterprise number in a variety of ways using voice input or image recognition. Voice input is achieved, for example, by using a voice recognition engine or a microphone. Image recognition is achieved, for example, by using OCR technology or a camera. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input voice data or image data into a generation AI and have the generation AI recognize the enterprise number.

[0036] When the user enters the company number, the reception unit can display an input guide according to the user's industry and company size. For example, if the user is a small or medium-sized enterprise, the reception unit can display a simplified input guide. Furthermore, if the user is a large enterprise, the reception unit can display a detailed input guide to cover all necessary information. Furthermore, the reception unit can customize the input guide by highlighting specific input items according to the user's industry. This allows the user to properly enter the company number by displaying an input guide according to the industry and company size. Examples of industry include, but are not limited to, manufacturing and services. Examples of company size include, but are not limited to, the number of employees and sales. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's industry and company size into a generation AI and have the generation AI customize the input guide.

[0037] When acquiring data, the data acquisition unit can select the optimal acquisition timing by taking into account the update frequency of the Ebis database. For example, if the Ebis database is updated daily, the data acquisition unit acquires data at a fixed time every day. Alternatively, if the Ebis database is updated in real time, the data acquisition unit can acquire data at the required timing. Alternatively, if the Ebis database is updated once a week, the data acquisition unit can acquire data once a week. This allows the latest data to be acquired at the appropriate timing by taking into account the update frequency of the Ebis database. The update frequency includes, but is not limited to, the database update interval and the freshness of the data. Some or all of the above-described processing in the data acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the data acquisition unit can input update frequency data of the Ebis database to the generation AI and have the generation AI select the optimal acquisition timing.

[0038] During data acquisition, the data acquisition unit can integrate and acquire information from multiple data sources. For example, the data acquisition unit acquires information from other related databases in addition to the Ebis database. The data acquisition unit can also integrate information acquired from multiple data sources and provide it as a single data set. The data acquisition unit can also acquire information from each data source in real time and integrate and provide it. This allows for more comprehensive data to be acquired by integrating information from multiple data sources. Examples of multiple data sources include, but are not limited to, external APIs and internal databases. Some or all of the above-described processing in the data acquisition unit may be performed using, or without, AI. For example, the data acquisition unit can input data acquired from multiple data sources into a generation AI and have the generation AI integrate the data.

[0039] When acquiring data, the data acquisition unit can evaluate the reliability of the acquired data and prioritize acquisition of highly reliable data. For example, the data acquisition unit can evaluate the reliability of each data source and prioritize acquisition of highly reliable data. The data acquisition unit can also use an algorithm to evaluate the reliability of data and select highly reliable data. The data acquisition unit can also evaluate the reliability of data and filter out unreliable data. This can ensure data quality by preferentially acquiring highly reliable data. Reliability includes, for example, the origin of data and data integrity, but is not limited to such examples. Some or all of the above-mentioned processing in the data acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the data acquisition unit can have a generation AI perform a reliability evaluation of the acquired data and select highly reliable data.

[0040] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis results. For example, the analysis unit selects an optimal analysis algorithm based on past analysis results. The analysis unit can also improve the analysis algorithm by referring to past analysis results. The analysis unit can also adjust parameters of the analysis algorithm based on past analysis results. This improves the accuracy of the analysis algorithm by referring to past analysis results. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis methods. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0041] During analysis, the analysis unit can apply different analysis methods depending on the data category. For example, the analysis unit can apply different analysis methods depending on the line type. The analysis unit can also apply different analysis methods depending on the location name. The analysis unit can also apply different analysis methods depending on the bandwidth. By applying an analysis method depending on the data category, the accuracy of the analysis is improved. Data categories include, but are not limited to, text data and numerical data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the data category into the generation AI and cause the generation AI to apply different analysis methods.

[0042] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. The analysis unit performs the analysis by taking into account, for example, the interrelationships between location names and line types. The analysis unit can also perform the analysis by taking into account the interrelationships between bandwidths and managed routers. The analysis unit can also perform the analysis by taking into account the interrelationships between data for each closed area. This improves the accuracy of the analysis by taking into account the interrelationships between data. Examples of interrelationships between data include, but are not limited to, correlation analysis and network analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] The visualization unit can adjust the level of detail of the visualization based on the importance of the data during visualization. For example, the visualization unit visualizes important data in detail. The visualization unit can also simplify and visualize data with low importance. The visualization unit can also adjust the level of visualization based on the importance of the data. This allows important information to be emphasized by adjusting the level of detail of the visualization based on the importance of the data. The importance of the data includes, for example, the influence and priority of the data, but is not limited to such examples. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input data on the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the visualization.

[0044] The visualization unit can apply different visualization methods depending on the data category during visualization. For example, the visualization unit can apply different visualization methods depending on the line type. The visualization unit can also apply different visualization methods depending on the location name. The visualization unit can also apply different visualization methods depending on the bandwidth. By applying a visualization method depending on the data category, a visually easy-to-understand configuration diagram can be obtained. Examples of visualization methods include, but are not limited to, heat maps and network graphs. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, AI. For example, the visualization unit can input data on the data category into a generation AI and cause the generation AI to apply different visualization methods.

[0045] During visualization, the visualization unit can improve the accuracy of the visualization by referring to the user's past visualization results. For example, the visualization unit selects an optimal visualization method based on the user's past visualization results. The visualization unit can also improve the visualization method by referring to the user's past visualization results. The visualization unit can also adjust visualization parameters based on the user's past visualization results. This improves the accuracy of the visualization by referring to the past visualization results. Past visualization results include, but are not limited to, past data sets and visualization history. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input past visualization result data into a generation AI and cause the generation AI to improve the accuracy of the visualization.

[0046] The providing unit can select the optimal providing method by referring to the user's past usage history when providing the data. The providing unit selects the optimal providing method based on, for example, the user's past usage history. The providing unit can also preferentially select a specific providing method based on the user's past usage history. The providing unit can also improve the providing method by referring to the user's past usage history. In this way, the optimal providing method for the user can be selected by referring to the past usage history. The usage history includes, for example, past operation logs and usage patterns, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history data into the generation AI and cause the generation AI to select the optimal providing method.

[0047] The providing unit can customize the provided content according to the user's current task when providing the information. The providing unit customizes the provided content according to, for example, the task the user is currently performing. The providing unit can also select optimal provided content based on the user's current task. The providing unit can also adjust the provided content taking the user's current task into consideration. This allows optimal information to be provided to the user by customizing the provided content according to the current task. The current task includes, for example, the type of task and its progress, but is not limited to such examples. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into a generation AI and cause the generation AI to customize the provided content.

[0048] The providing unit can improve the providing method by reflecting user feedback at the time of providing. The providing unit improves the providing method based on, for example, user feedback. The providing unit can also preferentially select a specific providing method based on user feedback. The providing unit can also adjust the providing method by reflecting user feedback. This makes it possible to improve the providing method by reflecting feedback. Feedback includes, for example, user surveys and post-use evaluations, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the providing method.

[0049] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing the data. For example, if the user is in a specific area, the providing unit can provide a network configuration diagram related to that area. Furthermore, if the user is moving, the providing unit can select the optimal delivery method based on the user's current location. Furthermore, if the user is in a specific city, the providing unit can provide a network configuration diagram related to that city. This allows the optimal delivery method for the user to be selected by taking into account the geographical location information. Geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's geographical location information data into the generating AI and cause the generating AI to select the optimal delivery method.

[0050] At the time of providing, the providing unit can analyze the user's social media activity and provide a related network configuration diagram. For example, the providing unit can provide a network configuration diagram related to companies the user follows on social media. The providing unit can also analyze the user's social media posts and provide a related network configuration diagram. The providing unit can also provide a related network configuration diagram by referring to the activities of the user's friends on social media. In this way, a network configuration diagram related to the user can be provided by analyzing social media activity. Social media activity includes, for example, analysis of post content and analysis of followers, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide a related network configuration diagram.

[0051] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. The providing unit customizes the delivery method based on, for example, the user's past feedback. The providing unit can also preferentially select a specific delivery method based on the user's past feedback. The providing unit can also adjust the delivery method by reflecting the user's past feedback. In this way, the delivery method can be customized by reflecting the past feedback. Past feedback includes, for example, past evaluation results and records of improvements, but is not limited to such examples. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the delivery method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When inputting a company number, the reception unit can perform input completion by referring to the user's past input history. For example, the reception unit can automatically display company numbers that the user has frequently input in the past as candidates. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest company numbers to be used during specific time periods based on the user's past input history. This reduces the effort required for input and enables efficient input by referring to the past input history. Input completion is achieved, for example, by an auto-complete function or by using past input data. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI perform input completion.

[0054] When entering a company number, the reception unit can diversify input methods using voice input or image recognition. For example, the user can simply enter the company number by voice, and the system will automatically recognize it. Alternatively, the user can enter the company number by hand and have it automatically converted to text using image recognition technology. Furthermore, the user can take a photo of a company logo with a camera and automatically obtain the company number using image recognition technology. This allows the user to enter the company number in a variety of ways using voice input and image recognition. Voice input is achieved, for example, using a voice recognition engine or a microphone. Image recognition is achieved, for example, using OCR technology or a camera. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice data or image data into a generation AI and have the generation AI recognize the company number.

[0055] When the user enters the company number, the reception unit can display an input guide according to the user's industry and company size. For example, if the user is a small or medium-sized enterprise, a simplified input guide can be displayed. Furthermore, if the user is a large enterprise, a detailed input guide can be displayed to cover all necessary information. Furthermore, the input guide can be customized by emphasizing specific input items according to the user's industry. This allows the user to properly enter the company number by displaying an input guide according to the industry and company size. Examples of industry include, but are not limited to, manufacturing and services. Examples of company size include, but are not limited to, the number of employees and sales. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's industry and company size into a generation AI and have the generation AI customize the input guide.

[0056] When acquiring data, the data acquisition unit can select the optimal acquisition timing by taking into account the update frequency of the Ebis database. For example, if the Ebis database is updated daily, the data acquisition unit can acquire data at a fixed time every day. Alternatively, if the Ebis database is updated in real time, the data can be acquired at the required timing. Furthermore, if the Ebis database is updated once a week, the data can be acquired once a week. This allows the latest data to be acquired at the appropriate timing by taking into account the update frequency of the Ebis database. The update frequency includes, but is not limited to, the database update interval and the freshness of the data. Some or all of the above-described processing in the data acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the data acquisition unit can input update frequency data of the Ebis database to the generation AI and have the generation AI select the optimal acquisition timing.

[0057] During data acquisition, the data acquisition unit can integrate and acquire information from multiple data sources. For example, in addition to the Ebis database, information can also be acquired from other related databases. Information acquired from multiple data sources can also be integrated and provided as a single data set. Furthermore, information from each data source can be acquired in real time and integrated and provided. This allows for more comprehensive data to be acquired by integrating information from multiple data sources. Examples of multiple data sources include, but are not limited to, external APIs and internal databases. Some or all of the above-described processing in the data acquisition unit may be performed using, or without, AI. For example, the data acquisition unit can input data acquired from multiple data sources into a generation AI and have the generation AI integrate the data.

[0058] When acquiring data, the data acquisition unit can evaluate the reliability of the acquired data and prioritize acquiring highly reliable data. For example, the reliability of each data source can be evaluated and highly reliable data can be acquired preferentially. Alternatively, an algorithm for evaluating the reliability of data can be used to select highly reliable data. Furthermore, the reliability of data can be evaluated and low reliability data can be filtered. This allows the quality of data to be ensured by preferentially acquiring highly reliable data. Reliability includes, but is not limited to, the origin of data and the integrity of data. Some or all of the above-described processing in the data acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the data acquisition unit can have a generation AI evaluate the reliability of the acquired data and select highly reliable data.

[0059] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis results. For example, the analysis unit selects an optimal analysis algorithm based on past analysis results. The analysis algorithm can also be improved by referring to past analysis results. Furthermore, the parameters of the analysis algorithm can be adjusted based on past analysis results. In this way, the accuracy of the analysis algorithm can be improved by referring to past analysis results. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis methods. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI and have the generation AI optimize the analysis algorithm.

[0060] During analysis, the analysis unit can apply different analysis methods depending on the data category. For example, different analysis methods can be applied depending on the line type. Also, different analysis methods can be applied depending on the location name. Furthermore, different analysis methods can be applied depending on the bandwidth. By applying an analysis method depending on the data category, the accuracy of the analysis can be improved. Data categories include, but are not limited to, text data and numerical data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the data category into the generation AI and have the generation AI apply different analysis methods.

[0061] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. For example, the analysis can be performed by taking into account the interrelationships between location names and line types. The analysis can also be performed by taking into account the interrelationships between bandwidths and managed routers. Furthermore, the analysis can be performed by taking into account the interrelationships between data for each closed area. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between data. Examples of interrelationships between data include, but are not limited to, correlation analysis and network analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the interrelationships between data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0062] The visualization unit can adjust the level of detail of the visualization based on the importance of the data during visualization. For example, important data can be visualized in detail. Also, data with low importance can be visualized in a simplified manner. Furthermore, the visualization level can be adjusted based on the importance of the data. This allows important information to be emphasized by adjusting the level of detail of the visualization based on the importance of the data. The importance of the data includes, but is not limited to, the influence and priority of the data. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, AI, or without AI. For example, the visualization unit can input data on the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the visualization.

[0063] During visualization, the visualization unit can apply different visualization methods depending on the data category. For example, different visualization methods can be applied depending on the line type. Also, different visualization methods can be applied depending on the location name. Furthermore, different visualization methods can be applied depending on the bandwidth. By applying a visualization method depending on the data category, a visually easy-to-understand configuration diagram can be obtained. Visualization methods include, but are not limited to, heat maps and network graphs. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input data on the data category into a generation AI and cause the generation AI to apply different visualization methods.

[0064] During visualization, the visualization unit can improve the accuracy of the visualization by referring to the user's past visualization results. For example, the visualization unit can select an optimal visualization method based on the user's past visualization results. The visualization unit can also improve the visualization method by referring to the user's past visualization results. Furthermore, the visualization parameters can be adjusted based on the user's past visualization results. By referring to the past visualization results, the accuracy of the visualization can be improved. Examples of past visualization results include, but are not limited to, past data sets and visualization history. Some or all of the above-described processing in the visualization unit can be performed using, for example, AI, or without AI. For example, the visualization unit can input past visualization result data into a generation AI and cause the generation AI to improve the accuracy of the visualization.

[0065] The providing unit can select the optimal providing method by referring to the user's past usage history when providing data. For example, the optimal providing method can be selected based on the user's past usage history. It can also preferentially select a specific providing method based on the user's past usage history. It can also improve the providing method by referring to the user's past usage history. This allows the optimal providing method for the user to be selected by referring to the past usage history. The usage history includes, for example, past operation logs and usage patterns, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's past usage history data into the generation AI and have the generation AI select the optimal providing method.

[0066] The providing unit can customize the provided content according to the user's current task when providing the information. For example, the provided content can be customized according to the task the user is currently performing. The providing unit can also select optimal provided content based on the user's current task. Furthermore, the provided content can be adjusted taking the user's current task into consideration. By customizing the provided content according to the current task, optimal information can be provided to the user. The current task includes, for example, the type of task and its progress, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's current task data into a generating AI and have the generating AI customize the provided content.

[0067] The providing unit can improve the delivery method by reflecting user feedback at the time of delivery. For example, the delivery method can be improved based on user feedback. Also, a specific delivery method can be preferentially selected based on user feedback. Furthermore, the delivery method can be adjusted by reflecting user feedback. This makes it possible to improve the delivery method by reflecting feedback. Feedback includes, for example, user surveys and post-use evaluations, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to improve the delivery method.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The reception unit inputs a company number. The company number includes, for example, a company code or a registration number, but is not limited to these examples. Step 2: The data acquisition unit acquires base data from Ebis based on the company number entered by the reception unit. The base data includes, for example, basic company information and network configuration information, but is not limited to these examples. Step 3: The analysis unit analyzes the acquired base data and organizes information on the location name, line type, bandwidth, and associated managed router for each closed area. Closed areas include, but are not limited to, specific network segments and security zones. Step 4: The visualization unit generates a network diagram based on the analyzed data, including, but not limited to, the method of displaying nodes and edges and the level of detail of the diagram. Step 5: The providing unit provides the generated network configuration diagram to the user. For example, the providing unit displays the generated network configuration diagram to the user via a web application or a mobile application.

[0070] (Example 2) The enterprise account sales support system according to an embodiment of the present invention automatically generates and provides network configuration diagrams based on enterprise numbers. The system inputs enterprise numbers, retrieves base data from Ebis, and collects information on the location name, line type, bandwidth, and associated managed routers for each closed area. Based on this information, the system automatically generates network configuration diagrams. The generated configuration diagrams are visually easy to understand, allowing users to grasp at a glance the connection status, line type, bandwidth, and managed router location of each location. For example, when creating a network configuration diagram for a company, simply enter the enterprise number. The necessary data is automatically retrieved from Ebis and visualizes the information for each location. This allows sales representatives to quickly create accurate network configuration diagrams, enabling them to provide prompt and accurate proposals to customers. This system prevents manual errors and significantly improves work efficiency, making it a powerful support tool for enterprise account sales. This allows the enterprise account sales support system to automatically generate and provide network configuration diagrams based on enterprise numbers. For example, sales representatives can quickly create accurate network configuration diagrams, enabling them to provide prompt and accurate proposals to customers. It also helps prevent manual errors and significantly improves work efficiency.

[0071] An enterprise account sales support system according to an embodiment includes a reception unit, a data acquisition unit, an analysis unit, a visualization unit, and a provision unit. The reception unit inputs a company number. The company number includes, for example, a company code or a registration number, but is not limited to these examples. The data acquisition unit acquires base data from Ebis based on the company number input by the reception unit. The base data includes, for example, basic company information and network configuration information, but is not limited to these examples. The analysis unit analyzes the acquired base data and organizes information on the location name, line type, bandwidth, and associated managed router for each closed area. The closed area includes, for example, specific network segments and security zones, but is not limited to these examples. The visualization unit generates a network configuration diagram based on the analyzed data. The network configuration diagram includes, for example, a method for displaying nodes and edges, the level of detail of the diagram, and the like, but is not limited to these examples. The provision unit provides the generated network configuration diagram to a user. The provision unit displays the generated network configuration diagram to a user, for example, via a web application or a mobile application. As a result, the enterprise account sales support system according to the embodiment can automatically generate and provide a network configuration diagram based on the enterprise number. For example, sales representatives can create accurate network configuration diagrams in a short amount of time, enabling them to make prompt and accurate proposals to customers. This also prevents manual errors and significantly improves work efficiency.

[0072] The data acquisition unit can acquire base data from Ebis. The data acquisition unit acquires base data such as basic company information and network configuration information from Ebis, for example. Ebis is a database provided by a specific provider, and includes basic company information and network configuration information, for example. By acquiring base data from Ebis, it is possible to secure the information necessary for generating a network configuration diagram. Some or all of the above-described processing in the data acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the data acquisition unit may input the data acquired from Ebis into a generation AI and have the generation AI analyze the data.

[0073] The analysis unit analyzes the acquired base data and organizes information on the location name, line type, bandwidth, and associated managed router for each closed area. The analysis unit, for example, analyzes the acquired base data and organizes information on the location name, line type, bandwidth, and associated managed router for each closed area. Closed areas include, for example, specific network segments and security zones, but are not limited to these examples. Location names include, for example, office names and data center names, but are not limited to these examples. Line types include, for example, optical fiber lines and dedicated lines, but are not limited to these examples. Bandwidths include, for example, units such as Mbps and Gbps, but are not limited to these examples. Managed routers include, for example, managed routers and routers from specific vendors, but are not limited to these examples. By analyzing the base data and organizing the necessary information, the accuracy of the network configuration diagram is improved. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the acquired base data into the generation AI and have the generation AI analyze the data.

[0074] The visualization unit can generate a network configuration diagram based on the analyzed data. The visualization unit generates the network configuration diagram based on, for example, the analyzed data. The network configuration diagram may include, for example, but is not limited to, the method of displaying nodes and edges and the level of detail of the diagram. By generating the network configuration diagram based on the analyzed data, a visually easy-to-understand configuration diagram can be obtained. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit may input the analyzed data to a generation AI and cause the generation AI to generate a network configuration diagram.

[0075] The providing unit can provide the generated network configuration diagram to the user. For example, the providing unit displays the generated network configuration diagram to the user via a web application or a mobile application. By providing the generated network configuration diagram to the user, the user can quickly and accurately grasp the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated network configuration diagram to a generating AI and cause the generating AI to execute an optimal display method for providing the diagram to the user.

[0076] The reception unit can estimate the user's emotions and adjust the input interface for the enterprise number based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly enter the enterprise number. This adjusts the input interface according to the user's emotions, reducing the user's stress and improving input efficiency. 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. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0077] When entering a company number, the reception unit can perform input completion by referring to the user's past input history. For example, the reception unit can automatically display company numbers that the user has frequently entered in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest company numbers to be used during specific time periods based on the user's past input history. By referring to the past input history, this reduces the effort required for input and enables efficient input. Input completion is achieved, for example, by an auto-complete function or a method using past input data. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI perform input completion.

[0078] The reception unit can diversify input methods for the enterprise number by using voice input or image recognition. For example, the reception unit allows the user to simply input the enterprise number by voice, and the system automatically recognizes it. The reception unit can also allow the user to input the enterprise number by hand and automatically convert it into text using image recognition technology. The reception unit can also allow the user to take a photo of a company logo with a camera and automatically obtain the enterprise number using image recognition technology. This allows the user to input the enterprise number in a variety of ways using voice input or image recognition. Voice input is achieved, for example, by using a voice recognition engine or a microphone. Image recognition is achieved, for example, by using OCR technology or a camera. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input voice data or image data into a generation AI and have the generation AI recognize the enterprise number.

[0079] When the user enters the company number, the reception unit can display an input guide according to the user's industry and company size. For example, if the user is a small or medium-sized enterprise, the reception unit can display a simplified input guide. Furthermore, if the user is a large enterprise, the reception unit can display a detailed input guide to cover all necessary information. Furthermore, the reception unit can customize the input guide by highlighting specific input items according to the user's industry. This allows the user to properly enter the company number by displaying an input guide according to the industry and company size. Examples of industry include, but are not limited to, manufacturing and services. Examples of company size include, but are not limited to, the number of employees and sales. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's industry and company size into a generation AI and have the generation AI customize the input guide.

[0080] The data acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated user emotions. For example, if the user is relaxed, the data acquisition unit can immediately acquire data. Furthermore, if the user is in a hurry, the data acquisition unit can also quickly acquire data. Furthermore, if the user is feeling stressed, the data acquisition unit can also perform data acquisition in the background so as not to interfere with the user's operations. By adjusting the timing of data acquisition according to the user's emotions, data can be acquired without interfering with the user's operations. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data acquisition unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data acquisition.

[0081] When acquiring data, the data acquisition unit can select the optimal acquisition timing by taking into account the update frequency of the Ebis database. For example, if the Ebis database is updated daily, the data acquisition unit acquires data at a fixed time every day. Alternatively, if the Ebis database is updated in real time, the data acquisition unit can acquire data at the required timing. Alternatively, if the Ebis database is updated once a week, the data acquisition unit can acquire data once a week. This allows the latest data to be acquired at the appropriate timing by taking into account the update frequency of the Ebis database. The update frequency includes, but is not limited to, the database update interval and the freshness of the data. Some or all of the above-described processing in the data acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the data acquisition unit can input update frequency data of the Ebis database to the generation AI and have the generation AI select the optimal acquisition timing.

[0082] During data acquisition, the data acquisition unit can integrate and acquire information from multiple data sources. For example, the data acquisition unit acquires information from other related databases in addition to the Ebis database. The data acquisition unit can also integrate information acquired from multiple data sources and provide it as a single data set. The data acquisition unit can also acquire information from each data source in real time and integrate and provide it. This allows for more comprehensive data to be acquired by integrating information from multiple data sources. Examples of multiple data sources include, but are not limited to, external APIs and internal databases. Some or all of the above-described processing in the data acquisition unit may be performed using, or without, AI. For example, the data acquisition unit can input data acquired from multiple data sources into a generation AI and have the generation AI integrate the data.

[0083] When acquiring data, the data acquisition unit can evaluate the reliability of the acquired data and prioritize acquisition of highly reliable data. For example, the data acquisition unit can evaluate the reliability of each data source and prioritize acquisition of highly reliable data. The data acquisition unit can also use an algorithm to evaluate the reliability of data and select highly reliable data. The data acquisition unit can also evaluate the reliability of data and filter out unreliable data. This can ensure data quality by preferentially acquiring highly reliable data. Reliability includes, for example, the origin of data and data integrity, but is not limited to such examples. Some or all of the above-mentioned processing in the data acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the data acquisition unit can have a generation AI perform a reliability evaluation of the acquired data and select highly reliable data.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a quick analysis when the user is in a hurry. The analysis unit can also perform a simple analysis when the user is stressed so as not to interfere with the user's operations. By adjusting the analysis method according to the user's emotions, analysis can be performed without interfering with the user's operations. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis method.

[0085] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis results. For example, the analysis unit selects an optimal analysis algorithm based on past analysis results. The analysis unit can also improve the analysis algorithm by referring to past analysis results. The analysis unit can also adjust parameters of the analysis algorithm based on past analysis results. This improves the accuracy of the analysis algorithm by referring to past analysis results. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis methods. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0086] During analysis, the analysis unit can apply different analysis methods depending on the data category. For example, the analysis unit can apply different analysis methods depending on the line type. The analysis unit can also apply different analysis methods depending on the location name. The analysis unit can also apply different analysis methods depending on the bandwidth. By applying an analysis method depending on the data category, the accuracy of the analysis is improved. Data categories include, but are not limited to, text data and numerical data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the data category into the generation AI and cause the generation AI to apply different analysis methods.

[0087] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. The analysis unit performs the analysis by taking into account, for example, the interrelationships between location names and line types. The analysis unit can also perform the analysis by taking into account the interrelationships between bandwidths and managed routers. The analysis unit can also perform the analysis by taking into account the interrelationships between data for each closed area. This improves the accuracy of the analysis by taking into account the interrelationships between data. Examples of interrelationships between data include, but are not limited to, correlation analysis and network analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0088] The visualization unit can estimate the user's emotions and adjust the visualization expression method based on the estimated user's emotions. For example, the visualization unit can perform detailed visualization when the user is relaxed. The visualization unit can also perform simple visualization when the user is in a hurry. The visualization unit can also perform visualization that is easy to understand when the user is stressed. By adjusting the visualization expression method according to the user's emotions, the visualization can be made easy to understand for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the visualization unit can be performed using, for example, an AI, or without an AI. For example, the visualization unit can input the user's emotion data into the generation AI and have the generation AI adjust the visualization expression method.

[0089] The visualization unit can adjust the level of detail of the visualization based on the importance of the data during visualization. For example, the visualization unit visualizes important data in detail. The visualization unit can also simplify and visualize data with low importance. The visualization unit can also adjust the level of visualization based on the importance of the data. This allows important information to be emphasized by adjusting the level of detail of the visualization based on the importance of the data. The importance of the data includes, for example, the influence and priority of the data, but is not limited to such examples. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input data on the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the visualization.

[0090] The visualization unit can apply different visualization methods depending on the data category during visualization. For example, the visualization unit can apply different visualization methods depending on the line type. The visualization unit can also apply different visualization methods depending on the location name. The visualization unit can also apply different visualization methods depending on the bandwidth. By applying a visualization method depending on the data category, a visually easy-to-understand configuration diagram can be obtained. Examples of visualization methods include, but are not limited to, heat maps and network graphs. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, AI. For example, the visualization unit can input data on the data category into a generation AI and cause the generation AI to apply different visualization methods.

[0091] During visualization, the visualization unit can improve the accuracy of the visualization by referring to the user's past visualization results. For example, the visualization unit selects an optimal visualization method based on the user's past visualization results. The visualization unit can also improve the visualization method by referring to the user's past visualization results. The visualization unit can also adjust visualization parameters based on the user's past visualization results. This improves the accuracy of the visualization by referring to the past visualization results. Past visualization results include, but are not limited to, past data sets and visualization history. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input past visualization result data into a generation AI and cause the generation AI to improve the accuracy of the visualization.

[0092] The providing unit can estimate the user's emotions and adjust the display method of the network configuration diagram to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can display a detailed network configuration diagram. When the user is in a hurry, the providing unit can also display a simple network configuration diagram. When the user is stressed, the providing unit can also display a visually easy-to-understand network configuration diagram. By adjusting the display method according to the user's emotions, it is possible to provide a network configuration diagram that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0093] The providing unit can select the optimal providing method by referring to the user's past usage history when providing the data. The providing unit selects the optimal providing method based on, for example, the user's past usage history. The providing unit can also preferentially select a specific providing method based on the user's past usage history. The providing unit can also improve the providing method by referring to the user's past usage history. In this way, the optimal providing method for the user can be selected by referring to the past usage history. The usage history includes, for example, past operation logs and usage patterns, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history data into the generation AI and cause the generation AI to select the optimal providing method.

[0094] The providing unit can customize the provided content according to the user's current task when providing the information. The providing unit customizes the provided content according to, for example, the task the user is currently performing. The providing unit can also select optimal provided content based on the user's current task. The providing unit can also adjust the provided content taking the user's current task into consideration. This allows optimal information to be provided to the user by customizing the provided content according to the current task. The current task includes, for example, the type of task and its progress, but is not limited to such examples. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into a generation AI and cause the generation AI to customize the provided content.

[0095] The providing unit can improve the providing method by reflecting user feedback at the time of providing. The providing unit improves the providing method based on, for example, user feedback. The providing unit can also preferentially select a specific providing method based on user feedback. The providing unit can also adjust the providing method by reflecting user feedback. This makes it possible to improve the providing method by reflecting feedback. Feedback includes, for example, user surveys and post-use evaluations, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the providing method.

[0096] The providing unit can estimate the user's emotions and determine the priority of the network configuration diagrams to be provided based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can prioritize providing the most important network configuration diagram. Furthermore, if the user is relaxed, the providing unit can sequentially provide detailed network configuration diagrams. Furthermore, if the user is stressed, the providing unit can prioritize providing network configuration diagrams that are easy to understand visually. This allows important information to be provided preferentially by determining the priority according to the user's emotions. Priority can include, but is not limited to, an evaluation of importance and a determination of urgency. The emotion estimation is realized using an emotion estimation function, such as 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. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0097] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing the data. For example, if the user is in a specific area, the providing unit can provide a network configuration diagram related to that area. Furthermore, if the user is moving, the providing unit can select the optimal delivery method based on the user's current location. Furthermore, if the user is in a specific city, the providing unit can provide a network configuration diagram related to that city. This allows the optimal delivery method for the user to be selected by taking into account the geographical location information. Geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's geographical location information data into the generating AI and cause the generating AI to select the optimal delivery method.

[0098] At the time of providing, the providing unit can analyze the user's social media activity and provide a related network configuration diagram. For example, the providing unit can provide a network configuration diagram related to companies the user follows on social media. The providing unit can also analyze the user's social media posts and provide a related network configuration diagram. The providing unit can also provide a related network configuration diagram by referring to the activities of the user's friends on social media. In this way, a network configuration diagram related to the user can be provided by analyzing social media activity. Social media activity includes, for example, analysis of post content and analysis of followers, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide a related network configuration diagram.

[0099] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. The providing unit customizes the delivery method based on, for example, the user's past feedback. The providing unit can also preferentially select a specific delivery method based on the user's past feedback. The providing unit can also adjust the delivery method by reflecting the user's past feedback. In this way, the delivery method can be customized by reflecting the past feedback. Past feedback includes, for example, past evaluation results and records of improvements, but is not limited to such examples. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, data acquisition unit, analysis unit, visualization 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 reception unit is realized by the control unit 46A of the smart device 14 and inputs a company number. The data acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires base data from Ebis. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the base data and organizes information for each closed area. The visualization unit is realized by the specific processing unit 290 of the data processing device 12 and generates a network configuration diagram. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated network configuration diagram to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, data acquisition unit, analysis unit, visualization unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs a company number. The data acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires base data from Ebis. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the base data and organizes information for each closed area. The visualization unit is realized by the specific processing unit 290 of the data processing device 12 and generates a network configuration diagram. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated network configuration diagram to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, data acquisition unit, analysis unit, visualization 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 reception unit is realized by the control unit 46A of the headset type terminal 314 and inputs a company number. The data acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires base data from Ebis. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the base data and organizes information for each closed area. The visualization unit is realized by the specific processing unit 290 of the data processing device 12 and generates a network configuration diagram. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the generated network configuration diagram to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, data acquisition unit, analysis unit, visualization 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 reception unit is realized by the control unit 46A of the robot 414 and inputs a company number. The data acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires base data from Ebis. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the base data and organizes information for each closed area. The visualization unit is realized by the specific processing unit 290 of the data processing device 12 and generates a network configuration diagram. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated network configuration diagram to the user.

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

[0101] The reception unit can estimate the user's emotions and adjust the input interface for the enterprise number based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly enter the enterprise number. This adjusts the input interface according to the user's emotions, reducing the user's stress and improving input efficiency. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the reception unit can be performed using AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0102] The data acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated user emotions. For example, if the user is relaxed, data acquisition can be performed immediately. Furthermore, if the user is in a hurry, data acquisition can be performed quickly. Furthermore, if the user is stressed, data acquisition can be performed in the background so as not to interfere with the user's operations. By adjusting the timing of data acquisition according to the user's emotions, data can be acquired without interfering with the user's operations. 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. Some or all of the above-described processing in the data acquisition unit can be performed using, for example, an AI, or without an AI. For example, the data acquisition unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data acquisition.

[0103] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be performed. Furthermore, if the user is in a hurry, a quick analysis can be performed. Furthermore, if the user is stressed, a simple analysis can be performed so as not to interfere with the user's operations. By adjusting the analysis method according to the user's emotions, analysis can be performed without interfering with the user's operations. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis method.

[0104] The visualization unit can estimate the user's emotions and adjust the visualization expression method based on the estimated user's emotions. For example, if the user is relaxed, detailed visualization can be performed. If the user is in a hurry, simple visualization can be performed. Furthermore, if the user is stressed, visualization that is easy to understand can be performed. This allows the visualization expression method to be adjusted according to the user's emotions, making it easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, an AI, or without an AI. For example, the visualization unit can input the user's emotion data into the generation AI and have the generation AI adjust the visualization expression method.

[0105] The providing unit can estimate the user's emotions and adjust the display method of the network configuration diagram provided based on the estimated user's emotions. For example, if the user is relaxed, a detailed network configuration diagram can be displayed. If the user is in a hurry, a simple network configuration diagram can be displayed. Furthermore, if the user is stressed, a visually easy-to-understand network configuration diagram can be displayed. By adjusting the display method according to the user's emotions, a network configuration diagram that is easy for the user to understand can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0106] When inputting a company number, the reception unit can perform input completion by referring to the user's past input history. For example, the reception unit can automatically display company numbers that the user has frequently input in the past as candidates. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest company numbers to be used during specific time periods based on the user's past input history. This reduces the effort required for input and enables efficient input by referring to the past input history. Input completion is achieved, for example, by an auto-complete function or by using past input data. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI perform input completion.

[0107] When entering a company number, the reception unit can diversify input methods using voice input or image recognition. For example, the user can simply enter the company number by voice, and the system will automatically recognize it. Alternatively, the user can enter the company number by hand and have it automatically converted to text using image recognition technology. Furthermore, the user can take a photo of a company logo with a camera and automatically obtain the company number using image recognition technology. This allows the user to enter the company number in a variety of ways using voice input and image recognition. Voice input is achieved, for example, using a voice recognition engine or a microphone. Image recognition is achieved, for example, using OCR technology or a camera. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice data or image data into a generation AI and have the generation AI recognize the company number.

[0108] When the user enters the company number, the reception unit can display an input guide according to the user's industry and company size. For example, if the user is a small or medium-sized enterprise, a simplified input guide can be displayed. Furthermore, if the user is a large enterprise, a detailed input guide can be displayed to cover all necessary information. Furthermore, the input guide can be customized by emphasizing specific input items according to the user's industry. This allows the user to properly enter the company number by displaying an input guide according to the industry and company size. Examples of industry include, but are not limited to, manufacturing and services. Examples of company size include, but are not limited to, the number of employees and sales. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's industry and company size into a generation AI and have the generation AI customize the input guide.

[0109] When acquiring data, the data acquisition unit can select the optimal acquisition timing by taking into account the update frequency of the Ebis database. For example, if the Ebis database is updated daily, the data acquisition unit can acquire data at a fixed time every day. Alternatively, if the Ebis database is updated in real time, the data can be acquired at the required timing. Furthermore, if the Ebis database is updated once a week, the data can be acquired once a week. This allows the latest data to be acquired at the appropriate timing by taking into account the update frequency of the Ebis database. The update frequency includes, but is not limited to, the database update interval and the freshness of the data. Some or all of the above-described processing in the data acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the data acquisition unit can input update frequency data of the Ebis database to the generation AI and have the generation AI select the optimal acquisition timing.

[0110] During data acquisition, the data acquisition unit can integrate and acquire information from multiple data sources. For example, in addition to the Ebis database, information can also be acquired from other related databases. Information acquired from multiple data sources can also be integrated and provided as a single data set. Furthermore, information from each data source can be acquired in real time and integrated and provided. This allows for more comprehensive data to be acquired by integrating information from multiple data sources. Examples of multiple data sources include, but are not limited to, external APIs and internal databases. Some or all of the above-described processing in the data acquisition unit may be performed using, or without, AI. For example, the data acquisition unit can input data acquired from multiple data sources into a generation AI and have the generation AI integrate the data.

[0111] When acquiring data, the data acquisition unit can evaluate the reliability of the acquired data and prioritize acquiring highly reliable data. For example, the reliability of each data source can be evaluated and highly reliable data can be acquired preferentially. Alternatively, an algorithm for evaluating the reliability of data can be used to select highly reliable data. Furthermore, the reliability of data can be evaluated and low reliability data can be filtered. This allows the quality of data to be ensured by preferentially acquiring highly reliable data. Reliability includes, but is not limited to, the origin of data and the integrity of data. Some or all of the above-described processing in the data acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the data acquisition unit can have a generation AI evaluate the reliability of the acquired data and select highly reliable data.

[0112] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis results. For example, the analysis unit selects an optimal analysis algorithm based on past analysis results. The analysis algorithm can also be improved by referring to past analysis results. Furthermore, the parameters of the analysis algorithm can be adjusted based on past analysis results. In this way, the accuracy of the analysis algorithm can be improved by referring to past analysis results. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis methods. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI and have the generation AI optimize the analysis algorithm.

[0113] During analysis, the analysis unit can apply different analysis methods depending on the data category. For example, different analysis methods can be applied depending on the line type. Also, different analysis methods can be applied depending on the location name. Furthermore, different analysis methods can be applied depending on the bandwidth. By applying an analysis method depending on the data category, the accuracy of the analysis can be improved. Data categories include, but are not limited to, text data and numerical data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the data category into the generation AI and have the generation AI apply different analysis methods.

[0114] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. For example, the analysis can be performed by taking into account the interrelationships between location names and line types. The analysis can also be performed by taking into account the interrelationships between bandwidths and managed routers. Furthermore, the analysis can be performed by taking into account the interrelationships between data for each closed area. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between data. Examples of interrelationships between data include, but are not limited to, correlation analysis and network analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the interrelationships between data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0115] The visualization unit can adjust the level of detail of the visualization based on the importance of the data during visualization. For example, important data can be visualized in detail. Also, data with low importance can be visualized in a simplified manner. Furthermore, the visualization level can be adjusted based on the importance of the data. This allows important information to be emphasized by adjusting the level of detail of the visualization based on the importance of the data. The importance of the data includes, but is not limited to, the influence and priority of the data. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, AI, or without AI. For example, the visualization unit can input data on the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the visualization.

[0116] During visualization, the visualization unit can apply different visualization methods depending on the data category. For example, different visualization methods can be applied depending on the line type. Also, different visualization methods can be applied depending on the location name. Furthermore, different visualization methods can be applied depending on the bandwidth. By applying a visualization method depending on the data category, a visually easy-to-understand configuration diagram can be obtained. Visualization methods include, but are not limited to, heat maps and network graphs. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input data on the data category into a generation AI and cause the generation AI to apply different visualization methods.

[0117] During visualization, the visualization unit can improve the accuracy of the visualization by referring to the user's past visualization results. For example, the visualization unit can select an optimal visualization method based on the user's past visualization results. The visualization unit can also improve the visualization method by referring to the user's past visualization results. Furthermore, the visualization parameters can be adjusted based on the user's past visualization results. By referring to the past visualization results, the accuracy of the visualization can be improved. Examples of past visualization results include, but are not limited to, past data sets and visualization history. Some or all of the above-described processing in the visualization unit can be performed using, for example, AI, or without AI. For example, the visualization unit can input past visualization result data into a generation AI and cause the generation AI to improve the accuracy of the visualization.

[0118] The providing unit can select the optimal providing method by referring to the user's past usage history when providing data. For example, the optimal providing method can be selected based on the user's past usage history. It can also preferentially select a specific providing method based on the user's past usage history. It can also improve the providing method by referring to the user's past usage history. This allows the optimal providing method for the user to be selected by referring to the past usage history. The usage history includes, for example, past operation logs and usage patterns, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's past usage history data into the generation AI and have the generation AI select the optimal providing method.

[0119] The providing unit can customize the provided content according to the user's current task when providing the information. For example, the provided content can be customized according to the task the user is currently performing. The providing unit can also select optimal provided content based on the user's current task. Furthermore, the provided content can be adjusted taking the user's current task into consideration. By customizing the provided content according to the current task, optimal information can be provided to the user. The current task includes, for example, the type of task and its progress, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's current task data into a generating AI and have the generating AI customize the provided content.

[0120] The providing unit can improve the delivery method by reflecting user feedback at the time of delivery. For example, the delivery method can be improved based on user feedback. Also, a specific delivery method can be preferentially selected based on user feedback. Furthermore, the delivery method can be adjusted by reflecting user feedback. This makes it possible to improve the delivery method by reflecting feedback. Feedback includes, for example, user surveys and post-use evaluations, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to improve the delivery method.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The reception unit inputs a company number. The company number includes, for example, a company code or a registration number, but is not limited to these examples. Step 2: The data acquisition unit acquires base data from Ebis based on the company number entered by the reception unit. The base data includes, for example, basic company information and network configuration information, but is not limited to these examples. Step 3: The analysis unit analyzes the acquired base data and organizes information on the location name, line type, bandwidth, and associated managed router for each closed area. Closed areas include, but are not limited to, specific network segments and security zones. Step 4: The visualization unit generates a network diagram based on the analyzed data, including, but not limited to, the method of displaying nodes and edges and the level of detail of the diagram. Step 5: The providing unit provides the generated network configuration diagram to the user. For example, the providing unit displays the generated network configuration diagram to the user via a web application or a mobile application.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 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 reception desk where you can enter your company number; a data acquisition unit that acquires base data from Ebis based on the company number input by the reception unit; an analysis unit that analyzes the original data acquired by the data acquisition unit; a visualization unit that generates a network configuration diagram based on the data analyzed by the analysis unit; a providing unit that provides the network configuration diagram generated by the visualization unit. A system characterized by:

2. The data acquisition unit Get base data from Ebis 2. The system of claim 1.

3. The analysis unit Analyze the acquired base data and organize information on the base name, line type, bandwidth, and associated managed router for each closed area.

2. The system of claim 1.

4. The visualization unit Generate a network diagram based on the analyzed data 2. The system of claim 1.

5. The providing unit Provide the generated network configuration diagram to the user 2. The system of claim 1.

6. The reception unit The user's emotions are estimated, and the input interface for the enterprise number is adjusted based on the estimated user's emotions.

2. The system of claim 1.

7. The reception unit When entering a company number, the system will refer to the user's past input history to complete the input.

2. The system of claim 1.

8. The reception unit Diversify input methods for entering company numbers by using voice input and image recognition.

2. The system of claim 1.

Images