system

Abstract

The system according to this embodiment aims to understand the usage status of existing lines and automatically create the latest network configuration diagram. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a list of existing lines. The analysis unit analyzes the list of lines received by the reception unit and understands the connection status and usage status of each line. The generation unit automatically creates a network configuration diagram based on the results analyzed by the analysis unit. The provision unit provides the network configuration diagram generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to grasp the usage status of existing lines and create an up-to-date network configuration diagram.

[0005] The system according to the embodiment aims to grasp the usage status of existing lines and automatically create an up-to-date network configuration diagram.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a list of existing lines. The analysis unit analyzes the list of lines received by the reception unit and understands the connection status and usage status of each line. The generation unit automatically creates a network configuration diagram based on the results analyzed by the analysis unit. The provision unit provides the network configuration diagram generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can grasp the usage status of existing lines and automatically create the latest network configuration diagram. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The network configuration diagram creation system according to an embodiment of the present invention is a system that automatically creates the latest network configuration diagrams for existing customers using a generation AI. This system can create a network configuration diagram by loading a list of existing lines into the generation AI. Specifically, first, the list of existing lines is loaded into the generation AI. The generation AI analyzes the input list of lines and understands the connection status and usage status of each line. Next, the generation AI automatically creates a network configuration diagram based on the analysis results. The generated configuration diagram visually shows the connection status and usage status of each line and is provided in a format that can be easily understood by SEs and sales personnel. Furthermore, the generated configuration diagram can be used as a document to be shared during handover. This allows SEs and sales personnel receiving the handover to quickly understand the customer's usage status. This tool reduces the workload for SEs and sales personnel. Since the creation of network configuration diagrams, which was previously done manually, is automated, the work time is significantly reduced. In addition, the generated configuration diagram can always reflect the latest network configuration. For example, even if a new line is added or an existing line is changed, the generation AI automatically updates the configuration diagram, so the latest information can always be provided. Thus, a network diagram creation system utilizing generation AI is an effective means of reducing the workload of SEs and sales staff and quickly understanding customer usage patterns. As a result, the network diagram creation system significantly reduces the workload of SEs and sales staff and allows for rapid understanding of customer usage patterns.

[0029] The network configuration diagram creation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a list of existing lines. The reception unit can receive data in formats such as CSV or JSON as input. The analysis unit analyzes the list of lines received by the reception unit and understands the connection status and usage status of each line. The analysis unit performs the analysis based on, for example, methods for checking connection status and evaluation criteria for usage status. The generation unit automatically creates a network configuration diagram based on the results analyzed by the analysis unit. The generation unit creates the network configuration diagram using, for example, a generation AI. The generation AI can use technologies such as deep learning or rule-based generation. The provision unit provides the network configuration diagram generated by the generation unit. The provision unit can output the generated network configuration diagram in PDF format, for example. The provision unit can also share the generated network configuration diagram on the cloud. As a result, the network configuration diagram creation system can reduce the workload of SEs and sales personnel by automatically creating and providing network configuration diagrams based on a list of existing lines.

[0030] The reception unit accepts a list of existing lines. The reception unit can accept data in formats such as CSV or JSON as input. Specifically, users log into the system and upload a file containing the list of existing lines through a dedicated interface. The reception unit automatically recognizes the format of the uploaded file and checks the data's integrity. For example, in the case of a CSV file, it checks the header information of each column and verifies that all necessary fields are present. In the case of a JSON file, it validates whether the data structure is correct. This allows the reception unit to handle everything from data reception to initial verification, enabling the subsequent analysis unit to begin processing smoothly. The reception unit can also assign a timestamp upon data reception, enabling version control of the data. This makes it easy to identify changes by comparing with past data. Furthermore, the reception unit displays a confirmation message to the user, notifying them that the data has been successfully received. This allows users to provide data with confidence.

[0031] The analysis unit analyzes the list of lines received by the reception unit to understand the connection status and usage status of each line. The analysis unit performs analysis based on, for example, methods for checking connection status and evaluation criteria for usage status. Specifically, it analyzes technical parameters such as the destination IP address, port number, bandwidth, and latency of each line to evaluate the performance and stability of each line. The analysis unit uses AI to quickly analyze large amounts of data and detect abnormal patterns and trends. For example, it applies anomaly detection algorithms using deep learning to detect unusual connection and usage statuses at an early stage. In addition, the analysis unit can evaluate the current situation by comparing it with past data and understand trends in line deterioration and improvement. This allows the analysis unit to not only understand the situation in real time but also perform long-term trend analysis and predictions. Furthermore, the analysis unit provides a dashboard to visually display the analysis results, allowing users to intuitively understand the situation. In this way, the analysis unit consistently handles everything from data analysis to result provision, supporting user decision-making.

[0032] The generation unit automatically creates a network diagram based on the results analyzed by the analysis unit. The generation unit creates the network diagram using, for example, a generation AI. The generation AI can utilize technologies such as deep learning and rule-based generation. Specifically, the generation AI receives connection and usage data provided by the analysis unit as input and generates a network diagram. When using deep learning, it learns from past network diagram datasets and has the ability to generate new diagrams. In the case of rule-based generation, it creates diagrams based on predefined rules or templates. The generation unit appropriately arranges shapes, colors, labels, etc., to provide the generated network diagram in a format that is easy for the user to understand. For example, important connection points and lines can be highlighted with thick lines or different colors, and detailed information for each line can be displayed in a pop-up window. This allows the generation unit to automatically create accurate and visually easy-to-understand network diagrams based on the analysis results, aiding user comprehension. Furthermore, the generation unit has the ability to update the generated diagram in real time, allowing it to respond to network changes and the addition of new data. This allows the generation unit to always provide a configuration diagram that reflects the latest network conditions, improving the user's work efficiency.

[0033] The service provider provides the network configuration diagrams generated by the generation service provider. For example, the service provider can output the generated network configuration diagrams in PDF format. The service provider can also share the generated network configuration diagrams on the cloud. Specifically, the service provider exports the generated configuration diagrams in PDF format, making them available for users to download. Furthermore, by integrating with cloud storage services, the generated configuration diagrams can be saved on the cloud and shared with stakeholders. This allows users to access the latest network configuration diagrams anytime, anywhere. The service provider also has the function to send the generated configuration diagrams to stakeholders via email or messaging apps. This enables the rapid sharing of important information and facilitates smooth communication among stakeholders. In addition, the service provider has the function to collect feedback on the generated configuration diagrams and incorporate it into future generation. For example, it provides an interface that allows users to add comments to the configuration diagrams or request revisions. This enables the service provider to respond flexibly to user needs and improve the overall quality of the system.

[0034] The generation unit can automatically create a network configuration diagram using a generation AI. The generation unit creates a network configuration diagram using, for example, a generation AI. The generation AI can use technologies such as deep learning and rule-based generation. The generation AI analyzes the input list of lines and understands the connection status and usage status of each line. The generation AI automatically creates a network configuration diagram based on the analysis results. Thus, by using the generation AI, the creation of the network configuration diagram is automated. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can create a network configuration diagram using a generation AI model that takes a list of lines as input and outputs a network configuration diagram.

[0035] The service provider can output the generated network diagram in PDF format. For example, the service provider can output the generated network diagram in PDF format. The PDF format can include specifications such as font embedding and security settings. This makes it easier to share the generated network diagram by outputting it in PDF format. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can output the network diagram using a generation AI model that outputs the generated network diagram in PDF format.

[0036] The service provider can share the generated network diagram on the cloud. For example, the service provider can share the generated network diagram on the cloud. This makes it easier to access the generated network diagram by sharing it on the cloud. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can share the network diagram using a generative AI model that shares the generated network diagram on the cloud.

[0037] The generation unit can automatically update the network configuration diagram using a generation AI when a new line is added or an existing line is modified. For example, when a new line is added or an existing line is modified, the generation unit automatically updates the network configuration diagram using the generation AI. The generation AI analyzes the input list of lines and understands the connection status and usage status of each line. Based on the analysis results, the generation AI automatically updates the network configuration diagram. This ensures that the configuration diagram always reflects the latest information. Some or all of the above processing in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit can update the network configuration diagram using a generation AI model that takes a list of lines as input and outputs a network configuration diagram.

[0038] The analysis unit can accept data in CSV or JSON format as input. The specific data format specifications and acceptance methods for CSV and JSON formats include, for example, field definitions and data encoding. This allows for flexible data analysis by supporting diverse data formats. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can analyze data using a generative AI model that takes CSV or JSON data as input and outputs analysis results.

[0039] The reception department can analyze the user's past line list submission history and select the optimal reception method. For example, the reception department can prioritize accepting line list formats that the user has frequently submitted in the past. It can also prioritize suggesting submission methods (email, upload, etc.) that the user has used in the past. Furthermore, the reception department can prioritize accepting line list submissions made during specific time periods based on the user's past submission history. This improves user convenience by selecting the optimal reception method based on past submission history. Some or all of the above processing in the reception department may be performed using, for example, a generation AI, or without a generation AI. For example, the reception department can input the user's past submission history data into a generation AI and have the generation AI select the optimal reception method.

[0040] The reception unit can filter the list of available lines based on the user's current network usage when receiving the list. For example, the reception unit can accept an appropriate list of lines by considering the bandwidth of the network currently being used by the user. The reception unit can also analyze the user's current network load and accept the list of lines at the optimal time. Furthermore, the reception unit can filter out unnecessary list of lines based on the user's current network usage. In this way, by filtering based on the current network usage, an appropriate list of lines can be accepted. Some or all of the above processing in the reception unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reception unit can input the user's network usage data into a generation AI and have the generation AI perform the filtering.

[0041] The reception unit can prioritize receiving a list of lines that are highly relevant, taking into account the user's geographical location information. For example, the reception unit can prioritize receiving a list of lines that are close to the user's current location. The reception unit can also filter the list of lines that are highly relevant based on the user's geographical location information. Furthermore, if the user is in a specific region, the reception unit can prioritize receiving a list of lines related to that region. In this way, by considering geographical location information, the reception unit can prioritize receiving a list of lines that are highly relevant. Some or all of the above processing in the reception unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reception unit can input the user's geographical location information data into a generation AI and have the generation AI select a list of lines that are highly relevant.

[0042] The reception unit can analyze the user's social media activity when receiving a list of connections and accept relevant connections. For example, the reception unit can prioritize receiving connections that the user has mentioned on social media. The reception unit can also filter connections based on the user's social media activity to select the most relevant ones. Furthermore, if the user belongs to a specific social media group, the reception unit can prioritize receiving connections related to that group. This allows the reception unit to accept connections that are highly relevant by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not. For example, the reception unit can input the user's social media activity data into a generative AI and have the generative AI select relevant connections.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the lines during the analysis. For example, the analysis unit performs a detailed analysis for important lines. The analysis unit can also perform a simplified analysis for lines of lower importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the lines. This allows for a detailed analysis of important lines by adjusting the level of detail of the analysis based on the importance of the lines. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input line importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the line category during analysis. For example, for data lines, the analysis unit can analyze data transfer speed and bandwidth. For voice lines, the analysis unit can also analyze voice quality and latency. Furthermore, for security lines, the analysis unit can analyze encryption strength and authentication method. This allows for appropriate analysis according to the line category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input line category data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the lines during the analysis. For example, the analysis unit will prioritize the analysis of recently submitted lines. The analysis unit can also postpone the analysis of older line lists. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This allows for the prioritization of the analysis based on the submission date, thereby prioritizing the analysis of the most recent line lists. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input line submission date data into a generating AI and have the generating AI determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relationships between the lines during the analysis. For example, the analysis unit can prioritize the analysis of lines with high relevance. It can also postpone the analysis of lines with low relevance. Furthermore, the analysis unit can adjust the analysis schedule based on the relationships between the lines. This allows for prioritizing the analysis of lines with high relevance by adjusting the order of analysis based on the relationships between the lines. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the relationship data between lines into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The generation unit can adjust the level of detail in the generated configuration diagrams based on the importance of the lines during generation. For example, the generation unit generates configuration diagrams containing detailed information for important lines. The generation unit can also generate simplified configuration diagrams for less important lines. Furthermore, the generation unit can determine the priority of the configuration diagrams according to the importance of the lines. This allows for the generation of detailed configuration diagrams for important lines by adjusting the level of detail based on the importance of the lines. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input line importance data into a generation AI and have the generation AI adjust the level of detail in the configuration diagrams.

[0048] The generation unit can apply different generation algorithms depending on the line category during generation. For example, for data lines, the generation unit generates a configuration diagram that takes into account data transfer speed and bandwidth. The generation unit can also generate a configuration diagram that takes into account voice quality and latency for voice lines. Furthermore, for security lines, the generation unit can generate a configuration diagram that takes into account encryption strength and authentication method. By applying an appropriate generation algorithm according to the line category, the optimal network configuration diagram can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input line category data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0049] The generation unit can determine the priority of the configuration diagrams to be generated based on the submission date of the lines during generation. For example, the generation unit generates configuration diagrams based on a list of recently submitted lines. The generation unit can also postpone the generation of older line lists. Furthermore, the generation unit can adjust the generation schedule of the configuration diagrams based on the submission date. This allows the latest line list to be reflected preferentially by determining the priority of the configuration diagrams based on the submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input line submission date data into a generation AI and have the generation AI perform the determination of the priority of the configuration diagrams.

[0050] The generation unit can adjust the order of the configuration diagrams it generates based on the relationships between the circuits. For example, the generation unit can prioritize reflecting circuits with high relevance in the configuration diagram. It can also postpone the generation of circuits with low relevance. Furthermore, the generation unit can adjust the generation schedule of the configuration diagrams based on the relationships between the circuits. This allows for the priority reflection of circuits with high relevance by adjusting the order of the configuration diagrams based on the relationships between the circuits. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input circuit relationship data into a generation AI and have the generation AI perform the adjustment of the order of the configuration diagrams.

[0051] The service provider can select the optimal display method by referring to the user's past operation history when providing the service. For example, the service provider can prioritize providing display methods that the user has used in the past. The service provider can also suggest the most frequently used display method based on the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and automatically select the optimal display method. This improves user convenience by selecting the optimal display method based on past operation history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's operation history data into a generation AI and have the generation AI select the optimal display method.

[0052] The service provider can select the optimal display method at the time of delivery, taking into account the user's current device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a desktop PC, the service provider can provide a high-resolution display method. This allows the service provider to provide a display method optimized for the user's device by considering device information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's device information data into a generation AI and have the generation AI select the optimal display method.

[0053] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a desktop PC, the service provider can provide a high-resolution display method. This allows the service provider to provide a display method optimized for the user's device by considering device information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input user device information data into a generation AI and have the generation AI select the optimal display method.

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

[0055] The reception desk can analyze a user's past network diagram usage history and suggest the most suitable diagram format. For example, if a user frequently used PDF diagrams in the past, the reception desk will prioritize suggesting PDF. If a user previously preferred cloud-based sharing, the reception desk can also suggest cloud sharing. Furthermore, if a user used diagrams during specific time periods, the reception desk can provide suggestions tailored to those times. This improves user convenience by providing optimal suggestions based on the user's past usage history.

[0056] The analysis unit can adjust the level of detail in the network configuration diagram based on the user's job responsibilities. For example, if the user belongs to the IT department, the analysis will include detailed technical information. If the user belongs to the sales department, a simplified analysis can be performed. Furthermore, if the user is in a management position, a more concise analysis can be performed. By adjusting the level of detail in the analysis according to the user's job responsibilities, the analysis results can be provided in a way that is easy for the user to understand.

[0057] The service provider can integrate the generated network diagrams with the user's calendar app. For example, the generated diagrams can be automatically added to a specific date in the calendar. Furthermore, notifications can be sent to the calendar when the diagrams are updated. Additionally, detailed information can be displayed when the user clicks on a diagram in the calendar. This simplifies the management of network diagrams and improves user convenience.

[0058] The service provider can customize the generated network configuration diagram according to the user's preferences. For example, if a user prefers a specific color or font, the service provider can provide a configuration diagram that reflects those settings. Similarly, if a user prefers a specific layout, the service provider can provide a configuration diagram that reflects that layout. Furthermore, if a user wishes to highlight specific information, the service provider can highlight that information. This allows for customization according to the user's preferences, thereby improving user satisfaction.

[0059] The service provider can enable users to operate the generated network configuration diagram using voice commands. For example, if a user says, "Show the next page," the next page will be displayed. Also, if a user says, "Highlight a specific line," that line will be highlighted. Furthermore, if a user says, "Save the configuration diagram," the diagram will be saved. This enables operation via voice commands, improving user convenience.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives a list of existing lines. The reception desk can accept data in formats such as CSV or JSON as input. Step 2: The analysis unit analyzes the list of lines received by the reception unit to understand the connection status and usage status of each line. The analysis unit performs the analysis based, for example, on methods for checking connection status and evaluation criteria for usage status. Step 3: The generation unit automatically creates a network diagram based on the results analyzed by the analysis unit. The generation unit creates the network diagram using, for example, a generation AI. The generation AI can use technologies such as deep learning or rule-based generation. Step 4: The provider unit provides the network configuration diagram generated by the generator unit. The provider unit can, for example, output the generated network configuration diagram in PDF format. The provider unit can also share the generated network configuration diagram on the cloud.

[0062] (Example of form 2) The network configuration diagram creation system according to an embodiment of the present invention is a system that automatically creates the latest network configuration diagrams for existing customers using a generation AI. This system can create a network configuration diagram by loading a list of existing lines into the generation AI. Specifically, first, the list of existing lines is loaded into the generation AI. The generation AI analyzes the input list of lines and understands the connection status and usage status of each line. Next, the generation AI automatically creates a network configuration diagram based on the analysis results. The generated configuration diagram visually shows the connection status and usage status of each line and is provided in a format that can be easily understood by SEs and sales personnel. Furthermore, the generated configuration diagram can be used as a document to be shared during handover. This allows SEs and sales personnel receiving the handover to quickly understand the customer's usage status. This tool reduces the workload for SEs and sales personnel. Since the creation of network configuration diagrams, which was previously done manually, is automated, the work time is significantly reduced. In addition, the generated configuration diagram can always reflect the latest network configuration. For example, even if a new line is added or an existing line is changed, the generation AI automatically updates the configuration diagram, so the latest information can always be provided. Thus, a network diagram creation system utilizing generation AI is an effective means of reducing the workload of SEs and sales staff and quickly understanding customer usage patterns. As a result, the network diagram creation system significantly reduces the workload of SEs and sales staff and allows for rapid understanding of customer usage patterns.

[0063] The network configuration diagram creation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a list of existing lines. The reception unit can receive data in formats such as CSV or JSON as input. The analysis unit analyzes the list of lines received by the reception unit and understands the connection status and usage status of each line. The analysis unit performs the analysis based on, for example, methods for checking connection status and evaluation criteria for usage status. The generation unit automatically creates a network configuration diagram based on the results analyzed by the analysis unit. The generation unit creates the network configuration diagram using, for example, a generation AI. The generation AI can use technologies such as deep learning or rule-based generation. The provision unit provides the network configuration diagram generated by the generation unit. The provision unit can output the generated network configuration diagram in PDF format, for example. The provision unit can also share the generated network configuration diagram on the cloud. As a result, the network configuration diagram creation system can reduce the workload of SEs and sales personnel by automatically creating and providing network configuration diagrams based on a list of existing lines.

[0064] The reception unit accepts a list of existing lines. The reception unit can accept data in formats such as CSV or JSON as input. Specifically, users log into the system and upload a file containing the list of existing lines through a dedicated interface. The reception unit automatically recognizes the format of the uploaded file and checks the data's integrity. For example, in the case of a CSV file, it checks the header information of each column and verifies that all necessary fields are present. In the case of a JSON file, it validates whether the data structure is correct. This allows the reception unit to handle everything from data reception to initial verification, enabling the subsequent analysis unit to begin processing smoothly. The reception unit can also assign a timestamp upon data reception, enabling version control of the data. This makes it easy to identify changes by comparing with past data. Furthermore, the reception unit displays a confirmation message to the user, notifying them that the data has been successfully received. This allows users to provide data with confidence.

[0065] The analysis unit analyzes the list of lines received by the reception unit to understand the connection status and usage status of each line. The analysis unit performs analysis based on, for example, methods for checking connection status and evaluation criteria for usage status. Specifically, it analyzes technical parameters such as the destination IP address, port number, bandwidth, and latency of each line to evaluate the performance and stability of each line. The analysis unit uses AI to quickly analyze large amounts of data and detect abnormal patterns and trends. For example, it applies anomaly detection algorithms using deep learning to detect unusual connection and usage statuses at an early stage. In addition, the analysis unit can evaluate the current situation by comparing it with past data and understand trends in line deterioration and improvement. This allows the analysis unit to not only understand the situation in real time but also perform long-term trend analysis and predictions. Furthermore, the analysis unit provides a dashboard to visually display the analysis results, allowing users to intuitively understand the situation. In this way, the analysis unit consistently handles everything from data analysis to result provision, supporting user decision-making.

[0066] The generation unit automatically creates a network diagram based on the results analyzed by the analysis unit. The generation unit creates the network diagram using, for example, a generation AI. The generation AI can utilize technologies such as deep learning and rule-based generation. Specifically, the generation AI receives connection and usage data provided by the analysis unit as input and generates a network diagram. When using deep learning, it learns from past network diagram datasets and has the ability to generate new diagrams. In the case of rule-based generation, it creates diagrams based on predefined rules or templates. The generation unit appropriately arranges shapes, colors, and labels to provide the generated network diagram in a user-friendly format. For example, important connection points and lines can be highlighted with thick lines or different colors, and detailed information for each line can be displayed in a pop-up window. This allows the generation unit to automatically create accurate and visually easy-to-understand network diagrams based on the analysis results, aiding user comprehension. Furthermore, the generation unit has the ability to update the generated diagram in real time, responding to network changes and the addition of new data. This allows the generation unit to always provide a configuration diagram that reflects the latest network conditions, improving the user's work efficiency.

[0067] The service provider provides the network configuration diagrams generated by the generation service provider. For example, the service provider can output the generated network configuration diagrams in PDF format. The service provider can also share the generated network configuration diagrams on the cloud. Specifically, the service provider exports the generated configuration diagrams in PDF format, making them available for users to download. Furthermore, by integrating with cloud storage services, the generated configuration diagrams can be saved on the cloud and shared with stakeholders. This allows users to access the latest network configuration diagrams anytime, anywhere. The service provider also has the function to send the generated configuration diagrams to stakeholders via email or messaging apps. This enables the rapid sharing of important information and facilitates smooth communication among stakeholders. In addition, the service provider has the function to collect feedback on the generated configuration diagrams and incorporate it into future generation. For example, it provides an interface that allows users to add comments to the configuration diagrams or request revisions. This enables the service provider to respond flexibly to user needs and improve the overall quality of the system.

[0068] The generation unit can automatically create a network configuration diagram using a generation AI. The generation unit creates a network configuration diagram using, for example, a generation AI. The generation AI can use technologies such as deep learning and rule-based generation. The generation AI analyzes the input list of lines and understands the connection status and usage status of each line. The generation AI automatically creates a network configuration diagram based on the analysis results. Thus, by using the generation AI, the creation of the network configuration diagram is automated. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can create a network configuration diagram using a generation AI model that takes a list of lines as input and outputs a network configuration diagram.

[0069] The service provider can output the generated network diagram in PDF format. For example, the service provider can output the generated network diagram in PDF format. The PDF format can include specifications such as font embedding and security settings. This makes it easier to share the generated network diagram by outputting it in PDF format. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can output the network diagram using a generation AI model that outputs the generated network diagram in PDF format.

[0070] The service provider can share the generated network diagram on the cloud. For example, the service provider can share the generated network diagram on the cloud. This makes it easier to access the generated network diagram by sharing it on the cloud. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can share the network diagram using a generative AI model that shares the generated network diagram on the cloud.

[0071] The generation unit can automatically update the network configuration diagram using a generation AI when a new line is added or an existing line is modified. For example, when a new line is added or an existing line is modified, the generation unit automatically updates the network configuration diagram using the generation AI. The generation AI analyzes the input list of lines and understands the connection status and usage status of each line. Based on the analysis results, the generation AI automatically updates the network configuration diagram. This ensures that the configuration diagram always reflects the latest information. Some or all of the above processing in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit can update the network configuration diagram using a generation AI model that takes a list of lines as input and outputs a network configuration diagram.

[0072] The analysis unit can accept data in CSV or JSON format as input. The specific data format specifications and acceptance methods for CSV and JSON formats include, for example, field definitions and data encoding. This allows for flexible data analysis by supporting diverse data formats. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can analyze data using a generative AI model that takes CSV or JSON data as input and outputs analysis results.

[0073] The reception unit can estimate the user's emotions and adjust the timing of accepting the call list based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the acceptance timing and wait until the user is relaxed. If the user is in a hurry, the reception unit can also speed up the acceptance timing to quickly accept the call list. Furthermore, if the user is relaxed, the reception unit can accept the call list at the normal timing. In this way, by adjusting the acceptance timing according to the user's emotions, the user's stress can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using a generative AI, or not using a generative AI. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception department can analyze the user's past line list submission history and select the optimal reception method. For example, the reception department can prioritize accepting line list formats that the user has frequently submitted in the past. It can also prioritize suggesting submission methods (email, upload, etc.) that the user has used in the past. Furthermore, the reception department can prioritize accepting line list submissions made during specific time periods based on the user's past submission history. This improves user convenience by selecting the optimal reception method based on past submission history. Some or all of the above processing in the reception department may be performed using, for example, a generation AI, or without a generation AI. For example, the reception department can input the user's past submission history data into a generation AI and have the generation AI select the optimal reception method.

[0075] The reception unit can filter the list of available lines based on the user's current network usage when receiving the list. For example, the reception unit can accept an appropriate list of lines by considering the bandwidth of the network currently being used by the user. The reception unit can also analyze the user's current network load and accept the list of lines at the optimal time. Furthermore, the reception unit can filter out unnecessary list of lines based on the user's current network usage. In this way, by filtering based on the current network usage, an appropriate list of lines can be accepted. Some or all of the above processing in the reception unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reception unit can input the user's network usage data into a generation AI and have the generation AI perform the filtering.

[0076] The reception desk can estimate the user's emotions and determine the priority of the call list to be received based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize important call lists. If the user is relaxed, the reception desk can also prioritize call lists with normal priority. Furthermore, if the user is in a hurry, the reception desk can prioritize urgent call lists. In this way, important call lists can be prioritized by determining priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using a generative AI, or not using a generative AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception unit can prioritize receiving a list of lines that are highly relevant, taking into account the user's geographical location information. For example, the reception unit can prioritize receiving a list of lines that are close to the user's current location. The reception unit can also filter the list of lines that are highly relevant based on the user's geographical location information. Furthermore, if the user is in a specific region, the reception unit can prioritize receiving a list of lines related to that region. In this way, by considering geographical location information, the reception unit can prioritize receiving a list of lines that are highly relevant. Some or all of the above processing in the reception unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reception unit can input the user's geographical location information data into a generation AI and have the generation AI select a list of lines that are highly relevant.

[0078] The reception unit can analyze the user's social media activity when receiving a list of connections and accept relevant connections. For example, the reception unit can prioritize receiving connections that the user has mentioned on social media. The reception unit can also filter connections based on the user's social media activity to select the most relevant ones. Furthermore, if the user belongs to a specific social media group, the reception unit can prioritize receiving connections related to that group. This allows the reception unit to accept connections that are highly relevant by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not. For example, the reception unit can input the user's social media activity data into a generative AI and have the generative AI select relevant connections.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the lines during the analysis. For example, the analysis unit performs a detailed analysis for important lines. The analysis unit can also perform a simplified analysis for lines of lower importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the lines. This allows for a detailed analysis of important lines by adjusting the level of detail of the analysis based on the importance of the lines. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input line importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the line category during analysis. For example, for data lines, the analysis unit can analyze data transfer speed and bandwidth. For voice lines, the analysis unit can also analyze voice quality and latency. Furthermore, for security lines, the analysis unit can analyze encryption strength and authentication method. This allows for appropriate analysis according to the line category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input line category data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can determine the priority of analysis based on the submission date of the lines during the analysis. For example, the analysis unit will prioritize the analysis of recently submitted lines. The analysis unit can also postpone the analysis of older line lists. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This allows for the prioritization of the analysis based on the submission date, thereby prioritizing the analysis of the most recent line lists. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input line submission date data into a generating AI and have the generating AI determine the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relationships between the lines during the analysis. For example, the analysis unit can prioritize the analysis of lines with high relevance. It can also postpone the analysis of lines with low relevance. Furthermore, the analysis unit can adjust the analysis schedule based on the relationships between the lines. This allows for prioritizing the analysis of lines with high relevance by adjusting the order of analysis based on the relationships between the lines. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the relationship data between lines into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0085] The generation unit can estimate the user's emotions and adjust the representation of the network diagram it generates based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a simple and highly visual diagram. If the user is relaxed, the generation unit can also generate a diagram containing detailed information. Furthermore, if the user is in a hurry, the generation unit can generate a diagram that gets straight to the point. By adjusting the representation of the network diagram according to the user's emotions, it is possible to provide a diagram that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0086] The generation unit can adjust the level of detail in the generated configuration diagrams based on the importance of the lines during generation. For example, the generation unit generates configuration diagrams containing detailed information for important lines. The generation unit can also generate simplified configuration diagrams for less important lines. Furthermore, the generation unit can determine the priority of the configuration diagrams according to the importance of the lines. This allows for the generation of detailed configuration diagrams for important lines by adjusting the level of detail based on the importance of the lines. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input line importance data into a generation AI and have the generation AI adjust the level of detail in the configuration diagrams.

[0087] The generation unit can apply different generation algorithms depending on the line category during generation. For example, for data lines, the generation unit generates a configuration diagram that takes into account data transfer speed and bandwidth. The generation unit can also generate a configuration diagram that takes into account voice quality and latency for voice lines. Furthermore, for security lines, the generation unit can generate a configuration diagram that takes into account encryption strength and authentication method. By applying an appropriate generation algorithm according to the line category, the optimal network configuration diagram can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input line category data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0088] The generation unit can estimate the user's emotions and adjust the length of the generated diagram based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise diagram. If the user is relaxed, the generation unit can also generate a longer diagram containing more detailed information. Furthermore, if the user is excited, the generation unit can generate a diagram with visually stimulating effects. By adjusting the length of the diagram according to the user's emotions, the generation unit can provide a diagram of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0089] The generation unit can determine the priority of the configuration diagrams to be generated based on the submission date of the lines during generation. For example, the generation unit generates configuration diagrams based on a list of recently submitted lines. The generation unit can also postpone the generation of older line lists. Furthermore, the generation unit can adjust the generation schedule of the configuration diagrams based on the submission date. This allows the latest line list to be reflected preferentially by determining the priority of the configuration diagrams based on the submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input line submission date data into a generation AI and have the generation AI perform the determination of the priority of the configuration diagrams.

[0090] The generation unit can adjust the order of the configuration diagrams it generates based on the relationships between the circuits. For example, the generation unit can prioritize reflecting circuits with high relevance in the configuration diagram. It can also postpone the generation of circuits with low relevance. Furthermore, the generation unit can adjust the generation schedule of the configuration diagrams based on the relationships between the circuits. This allows for the priority reflection of circuits with high relevance by adjusting the order of the configuration diagrams based on the relationships between the circuits. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input circuit relationship data into a generation AI and have the generation AI perform the adjustment of the order of the configuration diagrams.

[0091] The service provider can estimate the user's emotions and adjust the display method of the provided diagram based on the estimated user emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. In this way, by adjusting the display method according to the user's emotions, a highly visible display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The service provider can select the optimal display method by referring to the user's past operation history when providing the service. For example, the service provider can prioritize providing display methods that the user has used in the past. The service provider can also suggest the most frequently used display method based on the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and automatically select the optimal display method. This improves user convenience by selecting the optimal display method based on past operation history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's operation history data into a generation AI and have the generation AI select the optimal display method.

[0093] The service provider can select the optimal display method at the time of delivery, taking into account the user's current device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a desktop PC, the service provider can provide a high-resolution display method. This allows the service provider to provide a display method optimized for the user's device by considering device information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's device information data into a generation AI and have the generation AI select the optimal display method.

[0094] The service provider can estimate the user's emotions and adjust the operating procedures of the provided configuration diagram based on the estimated user emotions. For example, if the user is feeling stressed, the service provider can provide simple and intuitive operating procedures. It can also provide detailed operating procedures if the user is relaxed. Furthermore, if the user is in a hurry, it can provide procedures that allow for quick operation. By adjusting the operating procedures according to the user's emotions, the service provider can provide intuitive and user-friendly operating procedures. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a desktop PC, the service provider can provide a high-resolution display method. This allows the service provider to provide a display method optimized for the user's device by considering device information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input user device information data into a generation AI and have the generation AI select the optimal display method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reception desk can analyze a user's past network diagram usage history and suggest the most suitable diagram format. For example, if a user frequently used PDF diagrams in the past, the reception desk will prioritize suggesting PDF. If a user previously preferred cloud-based sharing, the reception desk can also suggest cloud sharing. Furthermore, if a user used diagrams during specific time periods, the reception desk can provide suggestions tailored to those times. This improves user convenience by providing optimal suggestions based on the user's past usage history.

[0098] The analysis unit can adjust the level of detail in the network configuration diagram based on the user's job responsibilities. For example, if the user belongs to the IT department, the analysis will include detailed technical information. If the user belongs to the sales department, a simplified analysis can be performed. Furthermore, if the user is in a management position, a more concise analysis can be performed. By adjusting the level of detail in the analysis according to the user's job responsibilities, the analysis results can be provided in a way that is easy for the user to understand.

[0099] The service provider can integrate the generated network diagrams with the user's calendar app. For example, the generated diagrams can be automatically added to a specific date in the calendar. Furthermore, notifications can be sent to the calendar when the diagrams are updated. Additionally, detailed information can be displayed when the user clicks on a diagram in the calendar. This simplifies the management of network diagrams and improves user convenience.

[0100] The service provider can customize the generated network configuration diagram according to the user's preferences. For example, if a user prefers a specific color or font, the service provider can provide a configuration diagram that reflects those settings. Similarly, if a user prefers a specific layout, the service provider can provide a configuration diagram that reflects that layout. Furthermore, if a user wishes to highlight specific information, the service provider can highlight that information. This allows for customization according to the user's preferences, thereby improving user satisfaction.

[0101] The service provider can enable users to operate the generated network configuration diagram using voice commands. For example, if a user says, "Show the next page," the next page will be displayed. Also, if a user says, "Highlight a specific line," that line will be highlighted. Furthermore, if a user says, "Save the configuration diagram," the diagram will be saved. This enables operation via voice commands, improving user convenience.

[0102] The reception desk can estimate the user's emotions and adjust the line list reception method based on the estimated emotions. For example, if the user is stressed, the line list reception can be done with simple operations. If the user is relaxed, it can request more detailed input. Furthermore, if the user is in a hurry, the reception can be completed quickly. In this way, by adjusting the reception method according to the user's emotions, user stress can be reduced.

[0103] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0104] The generation unit can estimate the user's emotions and adjust the layout of the generated network diagram based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible layout. If the user is relaxed, it can provide a layout with more detailed information. Furthermore, if the user is in a hurry, it can provide a layout that gets straight to the point. By adjusting the layout according to the user's emotions, it is possible to provide a network diagram that is easy for the user to understand.

[0105] The service provider can estimate the user's emotions and adjust the update frequency of the diagrams provided based on those emotions. For example, if the user is stressed, the update frequency can be reduced and the system can wait until the user is relaxed. If the user is relaxed, the diagrams can be provided at the normal update frequency. Furthermore, if the user is in a hurry, the update frequency can be increased to provide the latest information quickly. In this way, by adjusting the update frequency according to the user's emotions, user stress can be reduced.

[0106] The system can estimate the user's emotions and adjust the display method of the provided diagram based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that focuses on the essentials. By adjusting the display method according to the user's emotions, the system can provide a highly visible display method for the user.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives a list of existing lines. The reception desk can accept data in formats such as CSV or JSON as input. Step 2: The analysis unit analyzes the list of lines received by the reception unit to understand the connection status and usage status of each line. The analysis unit performs the analysis based, for example, on methods for checking connection status and evaluation criteria for usage status. Step 3: The generation unit automatically creates a network diagram based on the results analyzed by the analysis unit. The generation unit creates the network diagram using, for example, a generation AI. The generation AI can use technologies such as deep learning or rule-based generation. Step 4: The provider unit provides the network configuration diagram generated by the generator unit. The provider unit can, for example, output the generated network configuration diagram in PDF format. The provider unit can also share the generated network configuration diagram on the cloud.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives a list of existing lines. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the list of lines received by the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically creates a network configuration diagram based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated network configuration diagram. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives a list of existing lines. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the list of lines received by the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically creates a network configuration diagram based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated network configuration diagram. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives a list of existing lines. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the list of lines received by the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically creates a network configuration diagram based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated network configuration diagram. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives a list of existing lines. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the list of lines received by the reception unit. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically creates a network configuration diagram based on the analysis results. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the generated network configuration diagram. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The reception desk accepts a list of existing lines, An analysis unit analyzes the list of lines received by the reception unit and grasps the connection status and usage status of each line, A generation unit that automatically creates a network configuration diagram based on the results of the analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the network configuration diagram generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates network diagrams automatically. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Output the generated network diagram in PDF format. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Share the generated network diagram on the cloud. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The AI ​​generates and automatically updates the configuration diagram when a new line is added or an existing line is modified. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Accepts data in CSV or JSON format as input. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of accepting line requests based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is During the registration process, the system analyzes the user's past line list submission history to select the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving a list of connection requests, filtering is performed based on the user's current network usage. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the list of lines to be accepted based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a request for a list of lines, the system prioritizes requests for lines that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a list of connections, the system analyzes the user's social media activity and accepts relevant connections. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the connection. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the circuit. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the connection data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between the circuits. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate user emotions and adjust the representation of the network diagram generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, adjust the level of detail in the generated configuration diagram based on the importance of the network connection. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the line category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the generated diagram based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the priority of the configuration diagrams to be generated is determined based on the submission timing of the network connection. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, adjust the order of the generated configuration diagrams based on the relationships between the circuits. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the provided diagram is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's current device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the operating procedures of the provided configuration diagram based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk accepts a list of existing lines, An analysis unit analyzes the list of lines received by the reception unit and grasps the connection status and usage status of each line. A generation unit that automatically creates a network configuration diagram based on the results of the analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the network configuration diagram generated by the generation unit. A system characterized by the following features.

2. The generating unit is Network diagrams are automatically created using generation AI. The system according to feature 1.

3. The aforementioned supply unit is, Output the generated network diagram in PDF format. The system according to feature 1.

4. The aforementioned supply unit is, Share the generated network diagram on the cloud. The system according to feature 1.

5. The generating unit is The configuration diagram is automatically updated by the generating AI when a new line is added or an existing line is changed. The system according to feature 1.

6. The aforementioned analysis unit, Accepts data in CSV or JSON format as input. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of accepting line requests based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is During the application process, the system analyzes the user's past submission history of line lists and selects the most suitable application method. The system according to feature 1.

9. The aforementioned reception unit is When receiving a list of connection requests, filtering is performed based on the user's current network usage. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the list of incoming lines based on those estimated emotions. The system according to feature 1.

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