System

Abstract

To efficiently collect and visualize information of a corporation affected by line disconnection by specifying a cause of the line disconnection and performing preventive maintenance.SOLUTION: The channel search unit performs channel search. The ping test unit executes a ping test on the line retrieved by the line retrieval unit. The data analysis unit predicts an abnormality of the line based on a result of the ping test executed by the ping test unit. The action execution unit automatically generates a preventive maintenance schedule based on the abnormality predicted by the data analysis unit. The log receiving unit receives a log from the service device. The second data analyzing unit analyzes the log received by the log receiving unit and specifies an error message. The second action execution unit executes an action based on the error message specified by the second data analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to identify the cause of line outages and perform preventative maintenance, and there is room for improvement in efficiently collecting and visualizing information on businesses affected by line outages.

[0005] The system of the embodiment aims to identify the cause of line outages, perform preventative maintenance, and efficiently collect and visualize information on corporations affected by line outages. [Means for solving the problem]

[0006] The system according to the embodiment includes a line search unit, a ping test unit, a data analysis unit, an action execution unit, a log receiving unit, a second data analysis unit, a second action execution unit, a corporate counter unit, and a second visualization unit. The line search unit performs a line search. The ping test unit executes a ping test on the line searched by the line search unit. The data analysis unit predicts line abnormalities based on the results of the ping test executed by the ping test unit. The action execution unit automatically generates a preventive maintenance schedule based on the abnormalities predicted by the data analysis unit. The log receiving unit receives logs from the service equipment. The second data analysis unit analyzes the logs received by the log receiving unit and identifies error messages. The second action execution unit executes actions based on the error messages identified by the second data analysis unit. The corporate counter unit collects information on corporations affected by line outages. The second visualization unit visualizes data based on the information collected by the corporate counter unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify the cause of line outages, perform preventative maintenance, and efficiently collect and visualize information on corporations affected by line outages. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The system for identifying the cause of a line disconnection according to an embodiment of the present invention is a system that identifies the cause of a line disconnection based on logs and line information and visualizes the information, thereby enabling the system to quickly identify the cause of the line disconnection and take appropriate measures.

[0029] A system for identifying the cause of a line disconnection according to an embodiment includes a line search unit, a ping test unit, a data analysis unit, an action execution unit, a log reception unit, a second data analysis unit, a second action execution unit, a corporate counter unit, and a second visualization unit. The line search unit performs a line search. For example, the line search unit searches for a specific line and checks its status. The ping test unit executes a ping test on the line searched by the line search unit. For example, the ping test unit executes a ping on a specific IP address and records the response time. The data analysis unit predicts line abnormalities based on the results of the ping test executed by the ping test unit. For example, the data analysis unit analyzes the results of the ping test and detects abnormal response times and packet loss patterns. The action execution unit automatically generates a preventive maintenance schedule based on the abnormalities predicted by the data analysis unit. For example, the action execution unit performs maintenance before an abnormality occurs. The log reception unit receives logs from service equipment. For example, the log reception unit receives logs from specific service equipment and analyzes their contents. The second data analysis unit analyzes the log received by the log receiving unit and identifies an error message. For example, the second data analysis unit detects an abnormal pattern from the log data and identifies an error message. The second action execution unit executes an action based on the error message identified by the second data analysis unit. For example, the second action execution unit automatically solves the problem based on the error message. The corporate counter unit uses corporate counters to collect information on corporations affected by line outages. For example, the corporate counter unit monitors the network usage status of corporations and quickly grasps the impact of line outages when they occur. The second visualization unit visualizes data based on the information collected by the corporate counter unit. For example, the second visualization unit converts the collected data into graphs or charts to visualize the cause of the line outage. This allows the line outage cause identification system according to the embodiment to quickly identify the cause of the line outage and take appropriate measures. For example, the cause of the line outage can be intuitively understood and quickly addressed.

[0030] The ping test unit can predict line abnormalities based on the ping test results and automatically generate a preventive maintenance schedule based on the abnormalities. For example, the ping test unit analyzes the ping test results and detects abnormal response times and packet loss patterns. Based on this, it predicts line abnormalities and automatically generates a preventive maintenance schedule. For example, if abnormalities occur frequently during a specific time period, it plans maintenance for that time period. The ping test unit also learns from past ping test data and develops an algorithm that can detect signs of abnormalities early. This allows it to predict line abnormalities and automatically generate a preventive maintenance schedule. For example, it can perform maintenance before an abnormality occurs. The ping test unit also monitors the ping test results in real time and immediately generates a preventive maintenance schedule if an abnormality is detected. For example, it can plan maintenance immediately after an abnormality occurs and respond quickly. This makes it possible to predict line abnormalities and plan preventive maintenance, thereby preventing line outages.

[0031] The ping test unit can analyze the results of ping tests in real time and issue an immediate notification if an abnormality is detected. For example, the ping test unit will build a system that analyzes ping test results in real time and immediately notifies if an abnormal response time or packet loss is detected. For example, it will notify the administrator by email or SMS when an abnormality is detected. The ping test unit will also develop a system that monitors ping test results in real time and immediately issues an alert if an abnormality is detected. For example, it will display a warning on the dashboard when an abnormality occurs. The ping test unit will also analyze ping test results in real time and build a system that immediately executes automatic action if an abnormality is detected. For example, it will automatically restart the line when an abnormality occurs. This makes it possible to detect abnormalities in real time and immediately notify, enabling a rapid response.

[0032] The line search unit and ping test unit can provide an interface that allows operation using voice commands. A system is developed for the line search unit and ping test unit that provides an interface that allows operation using voice commands, for example, when performing a line search and ping test. For example, a user gives a voice command such as "Ping a specific IP address." The line search unit and ping test unit also use voice recognition technology to build an interface that allows operation using voice commands when performing a line search and ping test. For example, a user gives a voice command such as "Start line search." The line search unit and ping test unit also develop a system that provides an interface that allows operation using voice commands when performing a line search and ping test, allowing the user to easily operate using voice. For example, a user gives a voice command such as "Display the results of the ping test." This provides an interface that the user can easily operate using voice commands.

[0033] The line search unit and ping test unit can automatically configure settings to support different network protocols based on the results of the line search and ping test. The line search unit and ping test unit, for example, build a system that automatically configures settings to support different network protocols (e.g., IPv6) based on the results of the line search and ping test. For example, automatic migration from IPv4 to IPv6 is performed. The line search unit and ping test unit also develop an algorithm that analyzes the results of the ping test and automatically configures settings to support different network protocols. For example, IPv6-compatible settings are automatically applied. The line search unit and ping test unit also develop a system that automatically configures settings to support different network protocols based on the results of the line search and ping test, thereby eliminating the need for users to manually configure settings. For example, IPv6-compatible settings are automatically configured. This automatically configures settings to support different network protocols, thereby reducing user effort.

[0034] When analyzing the logs, the log receiving unit can use an anomaly detection algorithm to detect problems before they occur and propose preventive actions. For example, when analyzing the logs, the log receiving unit uses an anomaly detection algorithm to build a system that detects problems before they occur. For example, it detects abnormal patterns from log data and proposes preventive actions. Furthermore, when analyzing the logs, the log receiving unit uses an anomaly detection algorithm to develop an algorithm that detects signs of problems early. For example, it detects abnormal events from log data and proposes preventive actions. Furthermore, when analyzing the logs, the log receiving unit uses an anomaly detection algorithm to develop a system that detects problems before they occur and proposes preventive actions. For example, it detects abnormal trends from log data and proposes preventive actions. In this way, by detecting problems before they occur and proposing preventive actions, the stability of the system is improved.

[0035] When analyzing a log, the log receiving unit compares it with past log data to identify abnormality patterns, thereby enabling more accurate cause identification. For example, when analyzing a log, the log receiving unit builds a system that compares it with past log data to identify abnormality patterns. For example, it extracts abnormal patterns from past log data and compares them with current log data. Furthermore, when analyzing a log, the log receiving unit develops an algorithm that compares it with past log data to identify abnormality patterns. For example, it extracts abnormal events from past log data and compares them with current log data. Furthermore, when analyzing a log, the log receiving unit develops a system that compares it with past log data to identify abnormality patterns, thereby enabling more accurate cause identification. For example, it extracts abnormal trends from past log data and compares them with current log data. In this way, by comparing it with past log data, abnormal patterns can be identified, enabling more accurate cause identification.

[0036] The log receiving unit can incorporate an automatic translation function so that it can handle log data in different languages ​​when analyzing logs. For example, the log receiving unit builds a system that incorporates an automatic translation function so that it can handle log data in different languages ​​when analyzing logs. For example, English log data is automatically translated into Japanese. The log receiving unit also uses automatic translation technology so that it can handle log data in different languages ​​when analyzing logs. For example, French log data is automatically translated into English. The log receiving unit also develops a system that incorporates an automatic translation function so that it can handle log data in different languages ​​when analyzing logs, allowing users to analyze log data in multiple languages. For example, Chinese log data is automatically translated into Japanese. In this way, by incorporating an automatic translation function so that it can handle log data in different languages, global support is possible.

[0037] The log receiving unit can also include multimedia data such as audio logs and video logs in its analysis targets when analyzing logs. For example, the log receiving unit builds a system that includes multimedia data such as audio logs and video logs in its analysis targets when analyzing logs. For example, it detects abnormal audio patterns from audio logs. Furthermore, the log receiving unit uses multimedia analysis technology to include data such as audio logs and video logs in its analysis targets when analyzing logs. For example, it detects abnormal behavior from video logs. Furthermore, the log receiving unit develops a system that includes multimedia data such as audio logs and video logs in its analysis targets when analyzing logs, allowing users to analyze a variety of data. For example, it detects abnormal audio events from audio logs. As a result, by including multimedia data such as audio logs and video logs in its analysis targets, more diverse data analysis becomes possible.

[0038] The corporate counter department can analyze a corporation's network usage patterns and propose optimal network resource allocation. The corporate counter department, for example, analyzes a corporation's network usage patterns and builds a system that proposes optimal network resource allocation. For example, by distributing traffic during peak hours. The corporate counter department also analyzes a corporation's network usage patterns and develops an algorithm that proposes optimal network resource allocation. For example, by concentrating resources during specific time periods. The corporate counter department also analyzes a corporation's network usage patterns and develops a system that proposes optimal network resource allocation, thereby improving the corporation's network efficiency. For example, by preventing overuse of resources. In this way, network efficiency is improved by analyzing a corporation's network usage patterns and proposing optimal network resource allocation.

[0039] The corporate counter department can automatically generate customized network monitoring settings according to the business operations of a corporation. The corporate counter department, for example, builds a system that automatically generates customized network monitoring settings according to the business operations of a corporation. For example, it automatically configures monitoring items required for specific operations. The corporate counter department also develops an algorithm that automatically generates customized network monitoring settings according to the business operations of a corporation. For example, it automatically generates monitoring settings according to the importance of the operations. The corporate counter department also develops a system that automatically generates customized network monitoring settings according to the business operations of a corporation, thereby improving the business efficiency of the corporation. For example, it automatically generates monitoring settings according to the characteristics of the operations. In this way, business efficiency is improved by automatically generating customized network monitoring settings according to the business operations of a corporation.

[0040] The Corporate Counter Department can provide customizable settings so that it can accommodate corporations in different industries. For example, the Corporate Counter Department builds a system that provides customizable settings so that it can accommodate corporations in different industries. For example, it accommodates the different needs of manufacturing and service industries. The Corporate Counter Department also develops an algorithm that provides customizable settings so that it can accommodate corporations in different industries. For example, it automatically generates settings according to the characteristics of each industry. The Corporate Counter Department also develops a system that provides customizable settings so that it can accommodate corporations in different industries, thereby improving corporate business efficiency. For example, it provides settings according to the needs of each industry. In this way, business efficiency is improved by providing customizable settings so that it can accommodate corporations in different industries.

[0041] The corporate counter department can visualize the corporate network usage status in real time and provide it in dashboard format. The corporate counter department, for example, builds a system that visualizes the corporate network usage status in real time and provides it in dashboard format. For example, it displays traffic volume and connection status in graphs. The corporate counter department also develops an algorithm that visualizes the corporate network usage status in real time and provides it in dashboard format. For example, it displays abnormal traffic in real time. The corporate counter department also develops a system that visualizes the corporate network usage status in real time and provides it in dashboard format, thereby streamlining corporate network management. For example, it displays real-time alerts. In this way, the corporate network usage status can be visualized in real time and provided in dashboard format, thereby streamlining network management.

[0042] When analyzing data, the data analysis unit can use an anomaly detection algorithm to detect problems before they occur and propose preventive measures. For example, when analyzing data, the data analysis unit builds a system that uses an anomaly detection algorithm to detect problems before they occur. For example, it detects abnormal patterns from data and proposes preventive measures. Furthermore, when analyzing data, the data analysis unit develops an algorithm that uses an anomaly detection algorithm to detect signs of problems early. For example, it detects abnormal events from data and proposes preventive measures. Furthermore, when analyzing data, the data analysis unit develops a system that uses an anomaly detection algorithm to detect problems before they occur and propose preventive measures. For example, it detects abnormal trends from data and proposes preventive measures. In this way, by detecting problems before they occur and proposing preventive measures, the stability of the system is improved.

[0043] When analyzing data, the data analysis unit compares it with past data to identify abnormality patterns, thereby enabling more accurate cause identification. For example, when analyzing data, the data analysis unit builds a system that compares it with past data to identify abnormality patterns. For example, it extracts abnormal patterns from past data and compares them with current data. Furthermore, when analyzing data, the data analysis unit develops an algorithm that compares it with past data to identify abnormality patterns. For example, it extracts abnormal events from past data and compares them with current data. Furthermore, when analyzing data, the data analysis unit develops a system that compares it with past data to identify abnormality patterns, thereby enabling more accurate cause identification. For example, it extracts abnormal trends from past data and compares them with current data. In this way, by comparing it with past data, abnormal patterns can be identified, enabling more accurate cause identification.

[0044] The visualization unit can employ responsive design when visualizing data so that it can be used on different devices. For example, the visualization unit builds a system that employs responsive design so that it can be used on different devices when visualizing data. For example, it optimizes the display on smartphones and tablets. The visualization unit also uses responsive design technology to enable it to be used on different devices when visualizing data. For example, it automatically adjusts the display on desktops and mobile devices. The visualization unit also develops a system that employs responsive design so that it can be used on different devices when visualizing data, allowing users to comfortably view data on any device. For example, it automatically adjusts the layout according to the screen size. In this way, by employing responsive design so that it can be used on different devices, users can comfortably view data on any device.

[0045] The visualization unit can provide a function to assist in data interpretation using a voice assistant when visualizing data. For example, the visualization unit builds a system that provides a function to assist in data interpretation using a voice assistant when visualizing data. For example, the voice assistant explains the main points of the data. The visualization unit also provides a function to assist in data interpretation when visualizing data using voice assistant technology. For example, the voice assistant explains trends in the data. The visualization unit also develops a system that provides a function to assist in data interpretation using a voice assistant when visualizing data, making it easier for users to understand the data. For example, the voice assistant points out anomalies in the data. In this way, using the voice assistant to assist in data interpretation makes it easier for users to understand the data.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The system for identifying the cause of a line outage can further be equipped with an anomaly detection unit. The anomaly detection unit can detect line anomalies in real time and immediately issue an alert when an anomaly occurs. For example, the anomaly detection unit monitors the line's response time and packet loss patterns, and notifies the administrator by email or SMS when an anomaly is detected. The anomaly detection unit can also display a warning on the dashboard when an anomaly occurs. Furthermore, the anomaly detection unit can automatically restart the line when an anomaly occurs. This allows the impact of a line outage to be minimized by detecting anomalies in real time and responding immediately.

[0048] The ping test unit can automatically configure settings to support different network protocols based on the results of the ping test. For example, the ping test unit can analyze the results of the ping test and build a system that automatically migrates from IPv4 to IPv6. The ping test unit can also develop an algorithm that automatically configures settings to support different network protocols. Furthermore, the ping test unit can develop a system that automatically configures settings to support different network protocols to save users the trouble of having to configure settings manually. This saves users time and effort by automatically configuring settings to support different network protocols.

[0049] The line search unit and ping test unit can provide an interface that allows operation using voice commands. For example, a system can be developed in which the line search unit and ping test unit provide an interface that allows operation using voice commands when performing a line search and ping test. A user can give a voice command such as "run a ping to a specific IP address." Also, using voice recognition technology, an interface can be constructed that allows operation using voice commands when performing a line search and ping test. Furthermore, a system can be developed that provides an interface that allows operation using voice commands so that the user can easily operate the system using voice commands. This makes it possible to provide an interface that the user can easily operate using voice commands.

[0050] When analyzing the logs, the log receiver can use an anomaly detection algorithm to detect problems before they occur and suggest preventive actions. For example, the log receiver can build a system that detects abnormal patterns from log data and suggests preventive actions. The log receiver can also develop an algorithm that detects early signs of problems. Furthermore, the log receiver can develop a system that detects abnormal trends from log data and suggests preventive actions. This makes it possible to detect problems before they occur and suggest preventive actions, thereby improving system stability.

[0051] When analyzing logs, the log receiver compares them with past log data to identify abnormal patterns, enabling more accurate cause identification. For example, the log receiver builds a system that extracts abnormal patterns from past log data and compares them with current log data. The log receiver can also develop an algorithm that extracts abnormal events from past log data and compares them with current log data. Furthermore, the log receiver can develop a system that extracts abnormal trends from past log data and compares them with current log data. This makes it possible to identify abnormal patterns by comparing with past log data, enabling more accurate cause identification.

[0052] The Corporate Counter Department can analyze a corporation's network usage patterns and propose optimal network resource allocation. For example, the Corporate Counter Department can build a system to distribute traffic during peak hours. The Corporate Counter Department can also develop algorithms to concentrate resources at specific times of the day. Furthermore, the Corporate Counter Department can develop systems to prevent overuse of resources. This allows the corporation to analyze its network usage patterns and propose optimal network resource allocation, thereby improving network efficiency.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The line search unit performs a line search. For example, the line search unit searches for a specific line and checks its status. Step 2: The ping test unit executes a ping test on the line searched by the line search unit. For example, the ping test unit executes a ping on a specific IP address and records the response time. Step 3: The data analysis unit predicts line abnormalities based on the results of the ping test executed by the ping test unit. For example, the data analysis unit analyzes the results of the ping test and detects abnormal response times and packet loss patterns. Step 4: The action execution unit automatically generates a preventive maintenance schedule based on the anomalies predicted by the data analysis unit. For example, the action execution unit may perform maintenance before an anomaly occurs. Step 5: The log receiving unit receives the log from the service device. For example, the log receiving unit receives the log from a specific service device and analyzes the contents of the log. Step 6: The second data analysis unit analyzes the log received by the log receiving unit and identifies an error message. For example, the second data analysis unit detects an abnormal pattern from the log data and identifies an error message. Step 7: The second action execution unit executes an action based on the error message identified by the second data analysis unit, for example, the second action execution unit automatically solves the problem based on the error message. Step 8: The Corporate Counter Department uses the Corporate Counter to collect information on corporations affected by the line outage. For example, the Corporate Counter Department monitors the network usage status of corporations and quickly grasps the impact of a line outage. Step 9: The second visualization unit visualizes the data based on the information collected by the corporate counter unit. For example, the second visualization unit converts the collected data into graphs and charts to visualize the cause of the line outage.

[0055] (Example 2) The system for identifying the cause of a line disconnection according to an embodiment of the present invention is a system that identifies the cause of a line disconnection based on logs and line information and visualizes the information, thereby enabling the system to quickly identify the cause of the line disconnection and take appropriate measures.

[0056] A system for identifying the cause of a line disconnection according to an embodiment includes a line search unit, a ping test unit, a data analysis unit, an action execution unit, a log reception unit, a second data analysis unit, a second action execution unit, a corporate counter unit, and a second visualization unit. The line search unit performs a line search. For example, the line search unit searches for a specific line and checks its status. The ping test unit executes a ping test on the line searched by the line search unit. For example, the ping test unit executes a ping on a specific IP address and records the response time. The data analysis unit predicts line abnormalities based on the results of the ping test executed by the ping test unit. For example, the data analysis unit analyzes the results of the ping test and detects abnormal response times and packet loss patterns. The action execution unit automatically generates a preventive maintenance schedule based on the abnormalities predicted by the data analysis unit. For example, the action execution unit performs maintenance before an abnormality occurs. The log reception unit receives logs from service equipment. For example, the log reception unit receives logs from specific service equipment and analyzes their contents. The second data analysis unit analyzes the log received by the log receiving unit and identifies an error message. For example, the second data analysis unit detects an abnormal pattern from the log data and identifies an error message. The second action execution unit executes an action based on the error message identified by the second data analysis unit. For example, the second action execution unit automatically solves the problem based on the error message. The corporate counter unit uses corporate counters to collect information on corporations affected by line outages. For example, the corporate counter unit monitors the network usage status of corporations and quickly grasps the impact of line outages when they occur. The second visualization unit visualizes data based on the information collected by the corporate counter unit. For example, the second visualization unit converts the collected data into graphs or charts to visualize the cause of the line outage. This allows the line outage cause identification system according to the embodiment to quickly identify the cause of the line outage and take appropriate measures. For example, the cause of the line outage can be intuitively understood and quickly addressed.

[0057] The ping test unit can predict line abnormalities based on the ping test results and automatically generate a preventive maintenance schedule based on the abnormalities. For example, the ping test unit analyzes the ping test results and detects abnormal response times and packet loss patterns. Based on this, it predicts line abnormalities and automatically generates a preventive maintenance schedule. For example, if abnormalities occur frequently during a specific time period, it plans maintenance for that time period. The ping test unit also learns from past ping test data and develops an algorithm that can detect signs of abnormalities early. This allows it to predict line abnormalities and automatically generate a preventive maintenance schedule. For example, it can perform maintenance before an abnormality occurs. The ping test unit also monitors the ping test results in real time and immediately generates a preventive maintenance schedule if an abnormality is detected. For example, it can plan maintenance immediately after an abnormality occurs and respond quickly. This makes it possible to predict line abnormalities and plan preventive maintenance, thereby preventing line outages.

[0058] The ping test unit can analyze the results of ping tests in real time and issue an immediate notification if an abnormality is detected. For example, the ping test unit will build a system that analyzes ping test results in real time and immediately notifies if an abnormal response time or packet loss is detected. For example, it will notify the administrator by email or SMS when an abnormality is detected. The ping test unit will also develop a system that monitors ping test results in real time and immediately issues an alert if an abnormality is detected. For example, it will display a warning on the dashboard when an abnormality occurs. The ping test unit will also analyze ping test results in real time and build a system that immediately executes automatic action if an abnormality is detected. For example, it will automatically restart the line when an abnormality occurs. This makes it possible to detect abnormalities in real time and immediately notify, enabling a rapid response.

[0059] The ping test unit can estimate a user's emotions based on the results of the ping test and propose measures to reduce the user's stress level when a line disconnection occurs. For example, the ping test unit develops an algorithm to estimate a user's emotions when a line disconnection occurs based on the results of the ping test. For example, it estimates that a user's stress level increases when the response time is long. The ping test unit also analyzes the results of the ping test and builds a system to estimate the user's emotions. Based on this, it proposes measures to reduce the user's stress level when a line disconnection occurs. For example, it provides appropriate notifications and advice to the user. The ping test unit also estimates a user's emotions in real time based on the results of the ping test and automatically generates measures to reduce the user's stress level when a line disconnection occurs. For example, it provides content to help the user relax. In this way, user satisfaction is improved by taking the user's emotions into consideration and proposing measures to reduce the stress level.

[0060] The line search unit and ping test unit can provide an interface that allows operation using voice commands. A system is developed for the line search unit and ping test unit that provides an interface that allows operation using voice commands, for example, when performing a line search and ping test. For example, a user gives a voice command such as "Ping a specific IP address." The line search unit and ping test unit also use voice recognition technology to build an interface that allows operation using voice commands when performing a line search and ping test. For example, a user gives a voice command such as "Start line search." The line search unit and ping test unit also develop a system that provides an interface that allows operation using voice commands when performing a line search and ping test, allowing the user to easily operate using voice. For example, a user gives a voice command such as "Display the results of the ping test." This provides an interface that the user can easily operate using voice commands.

[0061] The line search unit and ping test unit can automatically configure settings to support different network protocols based on the results of the line search and ping test. The line search unit and ping test unit, for example, build a system that automatically configures settings to support different network protocols (e.g., IPv6) based on the results of the line search and ping test. For example, automatic migration from IPv4 to IPv6 is performed. The line search unit and ping test unit also develop an algorithm that analyzes the results of the ping test and automatically configures settings to support different network protocols. For example, IPv6-compatible settings are automatically applied. The line search unit and ping test unit also develop a system that automatically configures settings to support different network protocols based on the results of the line search and ping test, thereby eliminating the need for users to manually configure settings. For example, IPv6-compatible settings are automatically configured. This automatically configures settings to support different network protocols, thereby reducing user effort.

[0062] The line search unit and ping test unit can estimate a user's emotions based on the results of the line search and ping test and provide feedback to elicit positive emotions. For example, the line search unit and ping test unit develop an algorithm to estimate a user's emotions based on the results of the line search and ping test and provide feedback to elicit positive emotions. For example, an encouraging message is displayed to the user when the ping test is successful. The line search unit and ping test unit also analyze the results of the ping test, build a system to estimate a user's emotions, and provide feedback to elicit positive emotions. For example, a compliment is displayed to the user when the ping test results are good. The line search unit and ping test unit also develop a system to estimate a user's emotions in real time based on the results of the line search and ping test and provide feedback to elicit positive emotions. For example, a positive message is displayed to the user when the ping test is successful. This takes the user's emotions into consideration and provides positive feedback, thereby improving user satisfaction.

[0063] When analyzing the logs, the log receiving unit can use an anomaly detection algorithm to detect problems before they occur and propose preventive actions. For example, when analyzing the logs, the log receiving unit uses an anomaly detection algorithm to build a system that detects problems before they occur. For example, it detects abnormal patterns from log data and proposes preventive actions. Furthermore, when analyzing the logs, the log receiving unit uses an anomaly detection algorithm to develop an algorithm that detects signs of problems early. For example, it detects abnormal events from log data and proposes preventive actions. Furthermore, when analyzing the logs, the log receiving unit uses an anomaly detection algorithm to develop a system that detects problems before they occur and proposes preventive actions. For example, it detects abnormal trends from log data and proposes preventive actions. In this way, by detecting problems before they occur and proposing preventive actions, the stability of the system is improved.

[0064] When analyzing a log, the log receiving unit compares it with past log data to identify abnormality patterns, thereby enabling more accurate cause identification. For example, when analyzing a log, the log receiving unit builds a system that compares it with past log data to identify abnormality patterns. For example, it extracts abnormal patterns from past log data and compares them with current log data. Furthermore, when analyzing a log, the log receiving unit develops an algorithm that compares it with past log data to identify abnormality patterns. For example, it extracts abnormal events from past log data and compares them with current log data. Furthermore, when analyzing a log, the log receiving unit develops a system that compares it with past log data to identify abnormality patterns, thereby enabling more accurate cause identification. For example, it extracts abnormal trends from past log data and compares them with current log data. In this way, by comparing it with past log data, abnormal patterns can be identified, enabling more accurate cause identification.

[0065] The log receiving unit can estimate the user's emotions when analyzing the log and propose measures to minimize the impact of actions based on the log on the user. For example, the log receiving unit develops an algorithm for estimating the user's emotions when analyzing the log and proposes measures to minimize the impact of actions based on the log on the user. For example, the log receiving unit reduces the stress that actions based on the log cause to the user. Furthermore, the log receiving unit builds a system for estimating the user's emotions when analyzing the log and proposes measures to minimize the impact of actions based on the log on the user. For example, the log receiving unit reduces the anxiety that actions based on the log cause to the user. Furthermore, the log receiving unit develops a system for estimating the user's emotions in real time when analyzing the log and proposes measures to minimize the impact of actions based on the log on the user. For example, the log receiving unit reduces the burden that actions based on the log cause to the user. In this way, user satisfaction is improved by taking the user's emotions into consideration and proposing measures to minimize the impact of actions based on the log on the user.

[0066] The log receiving unit can incorporate an automatic translation function so that it can handle log data in different languages ​​when analyzing logs. For example, the log receiving unit builds a system that incorporates an automatic translation function so that it can handle log data in different languages ​​when analyzing logs. For example, English log data is automatically translated into Japanese. The log receiving unit also uses automatic translation technology so that it can handle log data in different languages ​​when analyzing logs. For example, French log data is automatically translated into English. The log receiving unit also develops a system that incorporates an automatic translation function so that it can handle log data in different languages ​​when analyzing logs, allowing users to analyze log data in multiple languages. For example, Chinese log data is automatically translated into Japanese. In this way, by incorporating an automatic translation function so that it can handle log data in different languages, global support is possible.

[0067] The log receiving unit can also include multimedia data such as audio logs and video logs in its analysis targets when analyzing logs. For example, the log receiving unit builds a system that includes multimedia data such as audio logs and video logs in its analysis targets when analyzing logs. For example, it detects abnormal audio patterns from audio logs. Furthermore, the log receiving unit uses multimedia analysis technology to include data such as audio logs and video logs in its analysis targets when analyzing logs. For example, it detects abnormal behavior from video logs. Furthermore, the log receiving unit develops a system that includes multimedia data such as audio logs and video logs in its analysis targets when analyzing logs, allowing users to analyze a variety of data. For example, it detects abnormal audio events from audio logs. As a result, by including multimedia data such as audio logs and video logs in its analysis targets, more diverse data analysis becomes possible.

[0068] The log receiving unit can estimate the user's emotions when analyzing the log and provide feedback to elicit positive emotions. For example, the log receiving unit develops an algorithm for estimating the user's emotions when analyzing the log and provides feedback to elicit positive emotions. For example, if the log analysis result is good, an encouraging message is displayed to the user. The log receiving unit also builds a system for estimating the user's emotions when analyzing the log and provides feedback to elicit positive emotions. For example, if the log analysis result is good, a compliment is displayed to the user. The log receiving unit also develops a system for estimating the user's emotions in real time when analyzing the log and provides feedback to elicit positive emotions. For example, if the log analysis result is good, a positive message is displayed to the user. In this way, the user's emotions are taken into consideration and positive feedback is provided, thereby improving user satisfaction.

[0069] The corporate counter department can analyze a corporation's network usage patterns and propose optimal network resource allocation. The corporate counter department, for example, analyzes a corporation's network usage patterns and builds a system that proposes optimal network resource allocation. For example, by distributing traffic during peak hours. The corporate counter department also analyzes a corporation's network usage patterns and develops an algorithm that proposes optimal network resource allocation. For example, by concentrating resources during specific time periods. The corporate counter department also analyzes a corporation's network usage patterns and develops a system that proposes optimal network resource allocation, thereby improving the corporation's network efficiency. For example, by preventing overuse of resources. In this way, network efficiency is improved by analyzing a corporation's network usage patterns and proposing optimal network resource allocation.

[0070] The corporate counter department can automatically generate customized network monitoring settings according to the business operations of a corporation. The corporate counter department, for example, builds a system that automatically generates customized network monitoring settings according to the business operations of a corporation. For example, it automatically configures monitoring items required for specific operations. The corporate counter department also develops an algorithm that automatically generates customized network monitoring settings according to the business operations of a corporation. For example, it automatically generates monitoring settings according to the importance of the operations. The corporate counter department also develops a system that automatically generates customized network monitoring settings according to the business operations of a corporation, thereby improving the business efficiency of the corporation. For example, it automatically generates monitoring settings according to the characteristics of the operations. In this way, business efficiency is improved by automatically generating customized network monitoring settings according to the business operations of a corporation.

[0071] The corporate counter department can estimate the emotions of corporate employees and propose measures to minimize the impact of network failures on business operations. The corporate counter department, for example, develops an algorithm to estimate the emotions of corporate employees and proposes measures to minimize the impact of network failures on business operations. For example, reducing employee stress. The corporate counter department also builds a system to estimate the emotions of corporate employees and proposes measures to minimize the impact of network failures on business operations. For example, reducing employee anxiety. The corporate counter department also develops a system to estimate the emotions of corporate employees in real time and proposes measures to minimize the impact of network failures on business operations. For example, reducing employee burden. In this way, business efficiency is improved by taking employee emotions into consideration and proposing measures to minimize the impact of network failures on business operations.

[0072] The Corporate Counter Department can provide customizable settings so that it can accommodate corporations in different industries. For example, the Corporate Counter Department builds a system that provides customizable settings so that it can accommodate corporations in different industries. For example, it accommodates the different needs of manufacturing and service industries. The Corporate Counter Department also develops an algorithm that provides customizable settings so that it can accommodate corporations in different industries. For example, it automatically generates settings according to the characteristics of each industry. The Corporate Counter Department also develops a system that provides customizable settings so that it can accommodate corporations in different industries, thereby improving corporate business efficiency. For example, it provides settings according to the needs of each industry. In this way, business efficiency is improved by providing customizable settings so that it can accommodate corporations in different industries.

[0073] The corporate counter department can visualize the corporate network usage status in real time and provide it in dashboard format. The corporate counter department, for example, builds a system that visualizes the corporate network usage status in real time and provides it in dashboard format. For example, it displays traffic volume and connection status in graphs. The corporate counter department also develops an algorithm that visualizes the corporate network usage status in real time and provides it in dashboard format. For example, it displays abnormal traffic in real time. The corporate counter department also develops a system that visualizes the corporate network usage status in real time and provides it in dashboard format, thereby streamlining corporate network management. For example, it displays real-time alerts. In this way, the corporate network usage status can be visualized in real time and provided in dashboard format, thereby streamlining network management.

[0074] The corporate counter department can estimate the emotions of corporate employees and provide feedback to elicit positive emotions. The corporate counter department, for example, develops an algorithm to estimate the emotions of corporate employees and provides feedback to elicit positive emotions. For example, when network stability improves, an encouraging message is displayed to employees. The corporate counter department also builds a system to estimate the emotions of corporate employees and provides feedback to elicit positive emotions. For example, when network performance improves, a compliment is displayed to employees. The corporate counter department also develops a system to estimate the emotions of corporate employees in real time and provide feedback to elicit positive emotions. For example, when network stability improves, a positive message is displayed to employees. In this way, by taking employees' emotions into consideration and providing positive feedback, employee satisfaction is improved.

[0075] When analyzing data, the data analysis unit can use an anomaly detection algorithm to detect problems before they occur and propose preventive measures. For example, when analyzing data, the data analysis unit builds a system that uses an anomaly detection algorithm to detect problems before they occur. For example, it detects abnormal patterns from data and proposes preventive measures. Furthermore, when analyzing data, the data analysis unit develops an algorithm that uses an anomaly detection algorithm to detect signs of problems early. For example, it detects abnormal events from data and proposes preventive measures. Furthermore, when analyzing data, the data analysis unit develops a system that uses an anomaly detection algorithm to detect problems before they occur and propose preventive measures. For example, it detects abnormal trends from data and proposes preventive measures. In this way, by detecting problems before they occur and proposing preventive measures, the stability of the system is improved.

[0076] When analyzing data, the data analysis unit compares it with past data to identify abnormality patterns, thereby enabling more accurate cause identification. For example, when analyzing data, the data analysis unit builds a system that compares it with past data to identify abnormality patterns. For example, it extracts abnormal patterns from past data and compares them with current data. Furthermore, when analyzing data, the data analysis unit develops an algorithm that compares it with past data to identify abnormality patterns. For example, it extracts abnormal events from past data and compares them with current data. Furthermore, when analyzing data, the data analysis unit develops a system that compares it with past data to identify abnormality patterns, thereby enabling more accurate cause identification. For example, it extracts abnormal trends from past data and compares them with current data. In this way, by comparing it with past data, abnormal patterns can be identified, enabling more accurate cause identification.

[0077] The data analysis unit can estimate a user's emotions when analyzing data and propose measures to minimize the impact of data visualization on the user. For example, the data analysis unit develops an algorithm for estimating a user's emotions when analyzing data and proposes measures to minimize the impact of data visualization on the user. For example, the data analysis unit reduces the stress that data visualization causes to the user. The data analysis unit also builds a system for estimating a user's emotions when analyzing data and proposes measures to minimize the impact of data visualization on the user. For example, the data analysis unit reduces the anxiety that data visualization causes to the user. The data analysis unit also develops a system for estimating a user's emotions in real time when analyzing data and proposes measures to minimize the impact of data visualization on the user. For example, the data analysis unit reduces the burden that data visualization causes to the user. In this way, user satisfaction is improved by taking user emotions into consideration and proposing measures to minimize the impact of data visualization on the user.

[0078] The visualization unit can employ responsive design when visualizing data so that it can be used on different devices. For example, the visualization unit builds a system that employs responsive design so that it can be used on different devices when visualizing data. For example, it optimizes the display on smartphones and tablets. The visualization unit also uses responsive design technology to enable it to be used on different devices when visualizing data. For example, it automatically adjusts the display on desktops and mobile devices. The visualization unit also develops a system that employs responsive design so that it can be used on different devices when visualizing data, allowing users to comfortably view data on any device. For example, it automatically adjusts the layout according to the screen size. In this way, by employing responsive design so that it can be used on different devices, users can comfortably view data on any device.

[0079] The visualization unit can provide a function to assist in data interpretation using a voice assistant when visualizing data. For example, the visualization unit builds a system that provides a function to assist in data interpretation using a voice assistant when visualizing data. For example, the voice assistant explains the main points of the data. The visualization unit also provides a function to assist in data interpretation when visualizing data using voice assistant technology. For example, the voice assistant explains trends in the data. The visualization unit also develops a system that provides a function to assist in data interpretation using a voice assistant when visualizing data, making it easier for users to understand the data. For example, the voice assistant points out anomalies in the data. In this way, using the voice assistant to assist in data interpretation makes it easier for users to understand the data.

[0080] The visualization unit can estimate a user's emotions when visualizing data and provide feedback to elicit positive emotions. For example, the visualization unit develops an algorithm for estimating a user's emotions when visualizing data and provides feedback to elicit positive emotions. For example, an encouraging message is displayed to the user when the data visualization is good. The visualization unit also builds a system for estimating a user's emotions when visualizing data and provides feedback to elicit positive emotions. For example, a compliment is displayed to the user when the data visualization is good. The visualization unit also develops a system for estimating a user's emotions in real time when visualizing data and providing feedback to elicit positive emotions. For example, a positive message is displayed to the user when the data visualization is good. In this way, user satisfaction is improved by taking the user's emotions into consideration and providing positive feedback.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The system for identifying the cause of a line outage can further be equipped with an anomaly detection unit. The anomaly detection unit can detect line anomalies in real time and immediately issue an alert when an anomaly occurs. For example, the anomaly detection unit monitors the line's response time and packet loss patterns, and notifies the administrator by email or SMS when an anomaly is detected. The anomaly detection unit can also display a warning on the dashboard when an anomaly occurs. Furthermore, the anomaly detection unit can automatically restart the line when an anomaly occurs. This allows the impact of a line outage to be minimized by detecting anomalies in real time and responding immediately.

[0083] The ping test unit can estimate the user's emotions based on the results of the ping test and propose measures to reduce the user's stress level when a line disconnection occurs. For example, the ping test unit can estimate that a user's stress level will increase if the response time delay is long, and provide appropriate notifications and advice. The ping test unit can also provide content to help the user relax. Furthermore, the ping test unit can estimate the user's emotions in real time and automatically generate measures to reduce the stress level. This makes it possible to improve user satisfaction by taking the user's emotions into consideration and proposing measures to reduce the stress level.

[0084] The ping test unit can automatically configure settings to support different network protocols based on the results of the ping test. For example, the ping test unit can analyze the results of the ping test and build a system that automatically migrates from IPv4 to IPv6. The ping test unit can also develop an algorithm that automatically configures settings to support different network protocols. Furthermore, the ping test unit can develop a system that automatically configures settings to support different network protocols to save users the trouble of having to configure settings manually. This saves users time and effort by automatically configuring settings to support different network protocols.

[0085] The ping test unit can estimate the user's emotions based on the results of the ping test and provide feedback to elicit positive emotions. For example, the ping test unit can display an encouraging message to the user when the ping test is successful. The ping test unit can also display words of praise to the user when the ping test results are good. Furthermore, the ping test unit can develop a system that estimates the user's emotions in real time and provides feedback to elicit positive emotions. This makes it possible to improve user satisfaction by taking the user's emotions into consideration and providing positive feedback.

[0086] The line search unit and ping test unit can provide an interface that allows operation using voice commands. For example, a system can be developed in which the line search unit and ping test unit provide an interface that allows operation using voice commands when performing a line search and ping test. A user can give a voice command such as "run a ping to a specific IP address." Also, using voice recognition technology, an interface can be constructed that allows operation using voice commands when performing a line search and ping test. Furthermore, a system can be developed that provides an interface that allows operation using voice commands so that the user can easily operate the system using voice commands. This makes it possible to provide an interface that the user can easily operate using voice commands.

[0087] When analyzing the logs, the log receiver can use an anomaly detection algorithm to detect problems before they occur and suggest preventive actions. For example, the log receiver can build a system that detects abnormal patterns from log data and suggests preventive actions. The log receiver can also develop an algorithm that detects early signs of problems. Furthermore, the log receiver can develop a system that detects abnormal trends from log data and suggests preventive actions. This makes it possible to detect problems before they occur and suggest preventive actions, thereby improving system stability.

[0088] When analyzing the log, the log receiving unit can estimate the user's emotions and propose measures to minimize the impact of actions based on the log on the user. For example, the log receiving unit can propose measures to reduce stress that actions based on the log cause to the user. The log receiving unit can also propose measures to reduce anxiety that actions based on the log cause to the user. Furthermore, the log receiving unit can develop a system that estimates the user's emotions in real time and proposes measures to minimize the impact that actions based on the log cause to the user. This makes it possible to improve user satisfaction by taking the user's emotions into consideration and proposing measures to minimize the impact that actions based on the log cause to the user.

[0089] When analyzing logs, the log receiver compares them with past log data to identify abnormal patterns, enabling more accurate cause identification. For example, the log receiver builds a system that extracts abnormal patterns from past log data and compares them with current log data. The log receiver can also develop an algorithm that extracts abnormal events from past log data and compares them with current log data. Furthermore, the log receiver can develop a system that extracts abnormal trends from past log data and compares them with current log data. This makes it possible to identify abnormal patterns by comparing with past log data, enabling more accurate cause identification.

[0090] The Corporate Counter Department can analyze a corporation's network usage patterns and propose optimal network resource allocation. For example, the Corporate Counter Department can build a system to distribute traffic during peak hours. The Corporate Counter Department can also develop algorithms to concentrate resources at specific times of the day. Furthermore, the Corporate Counter Department can develop systems to prevent overuse of resources. This allows the corporation to analyze its network usage patterns and propose optimal network resource allocation, thereby improving network efficiency.

[0091] The corporate counter department can estimate the emotions of the company's employees and propose measures to minimize the impact of network failures on business operations. For example, the corporate counter department can propose measures to reduce employee stress. The corporate counter department can also propose measures to reduce employee anxiety. Furthermore, the corporate counter department can develop a system that proposes measures to reduce employee burden. This makes it possible to improve business efficiency by taking employee emotions into consideration and proposing measures to minimize the impact of network failures on business operations.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The line search unit performs a line search. For example, the line search unit searches for a specific line and checks its status. Step 2: The ping test unit executes a ping test on the line searched by the line search unit. For example, the ping test unit executes a ping on a specific IP address and records the response time. Step 3: The data analysis unit predicts line abnormalities based on the results of the ping test executed by the ping test unit. For example, the data analysis unit analyzes the results of the ping test and detects abnormal response times and packet loss patterns. Step 4: The action execution unit automatically generates a preventive maintenance schedule based on the anomalies predicted by the data analysis unit. For example, the action execution unit may perform maintenance before an anomaly occurs. Step 5: The log receiving unit receives the log from the service device. For example, the log receiving unit receives the log from a specific service device and analyzes the contents of the log. Step 6: The second data analysis unit analyzes the log received by the log receiving unit and identifies an error message. For example, the second data analysis unit detects an abnormal pattern from the log data and identifies an error message. Step 7: The second action execution unit executes an action based on the error message identified by the second data analysis unit, for example, the second action execution unit automatically solves the problem based on the error message. Step 8: The Corporate Counter Department uses the Corporate Counter to collect information on corporations affected by the line outage. For example, the Corporate Counter Department monitors the network usage status of corporations and quickly grasps the impact of a line outage. Step 9: The second visualization unit visualizes the data based on the information collected by the corporate counter unit. For example, the second visualization unit converts the collected data into graphs and charts to visualize the cause of the line outage.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a line search unit for performing a line search; a ping test unit that executes a ping test on the line searched by the line search unit; a data analysis unit that predicts line abnormalities based on the results of the ping test executed by the ping test unit; an action execution unit that automatically generates a preventive maintenance schedule based on the abnormality predicted by the data analysis unit; a log receiving unit that receives a log from the service device; a second data analysis unit that analyzes the log received by the log receiving unit and identifies an error message; a second action execution unit that executes an action based on the error message identified by the second data analysis unit; a corporate counter department that uses the corporate counter department to collect information on corporations affected by line interruptions; and a second visualization unit that visualizes data based on the information collected by the corporate counter unit. A system characterized by:

2. The ping test unit Based on the results of the ping test, an abnormality in the line is predicted, and a preventative maintenance schedule is automatically generated based on the abnormality.

2. The system of claim 1.

3. The ping test unit The results of the ping test are analyzed in real time, and if an abnormality is detected, a notification is sent immediately.

2. The system of claim 1.

4. The ping test unit Based on the results of the ping test, the user's emotions are estimated and measures are proposed to reduce the user's stress level when the line is disconnected.

2. The system of claim 1.

5. The line search unit and the ping test unit Provide an interface for operations using voice commands 2. The system of claim 1.

6. The line search unit and the ping test unit Based on the results of the line search and ping test, the system automatically configures the network to support different network protocols.

2. The system of claim 1.

7. The line search unit and the ping test unit Based on the results of the line search and the ping test, the system estimates the user's emotions and provides feedback to elicit positive emotions.

2. The system of claim 1.

8. The log receiving unit When analyzing the logs, anomaly detection algorithms are used to proactively detect problems and suggest preventative actions.

2. The system of claim 1.

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