System

Abstract

To provide a system for providing a prompt and appropriate response when a system failure occurs and to reduce the burden on operators.SOLUTION: The system according to an embodiment comprises a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit receives input of a failure status. The analysis unit analyzes the failure status input through the reception unit. The provision unit provides specific response methods based on the result analyzed by the analysis unit. The monitoring unit monitors a daily system status.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to take prompt and appropriate action when a system failure occurred, and this placed a heavy burden on operators.

[0005] The system according to the embodiment aims to provide a prompt and appropriate response when a system failure occurs, and to reduce the burden on the operator. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit inputs a fault condition. The analysis unit analyzes the fault condition input by the reception unit. The provision unit provides a specific response method based on the result of the analysis by the analysis unit. The monitoring unit monitors the system status on a daily basis. Effect of the Invention

[0007] The system according to the embodiment can provide a prompt and appropriate response when a system failure occurs, and reduce the burden on the operator. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that provides specific responses to system failures. This system will tell you specific ways to respond by simply inputting the failure status. In addition, by inputting the daily system status, the system itself will talk to you about "things that should be addressed" as necessary. This mechanism can prevent the cause investigation from taking too long and the impact from expanding due to incorrect operation. Furthermore, it also leads to improved service quality, reduced operator load, and increased sales through external sales. For example, the failure status is input. For example, a situation in which a specific part of the system does not work or an error message is displayed is input. This information is input to the generation AI. Next, the generation AI analyzes the input failure status and provides specific ways to respond. The generation AI proposes the optimal way to respond based on past failure data and procedure manuals. For example, it will specifically tell you which procedure to execute for a specific error message. In addition, by inputting the daily system status, the generation AI will talk to you about "things that should be addressed" as necessary. For example, if the system performance is deteriorating, the generation AI will identify the cause and propose a way to respond. In this way, the operator can always grasp the system status and take appropriate measures. This mechanism can prevent the cause investigation from taking too long and the impact from expanding due to incorrect operation. For example, when a failure occurs, the generation AI can quickly suggest a response method, allowing the operator to respond quickly. In addition, by monitoring the system status daily, it is possible to detect early signs of a failure and respond in advance. Furthermore, this mechanism also contributes to improving service quality. For example, by always keeping the system performance at an optimal state, the quality of service for users is improved. It also reduces the burden on operators. By the generation AI suggesting a specific response method, operators can save the trouble of having to refer to the procedure manual. Finally, this mechanism can also lead to increased sales through external sales. For example, by providing this system to other companies, sales can be increased. The specific response methods and daily system status monitoring functions provided by the generation AI are useful to many companies and are likely to be in high demand.This allows the system to prevent the investigation into the cause from being prolonged or the scope of impact from expanding due to incorrect operation.

[0029] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit inputs a fault condition. The fault condition includes, for example, a system error, a network fault, a hardware failure, and the like, but is not limited to such examples. The reception unit receives, for example, a fault condition input by a user, and transmits it to the analysis unit. The analysis unit uses a generation AI to analyze the fault condition input by the reception unit. The analysis is performed, for example, by a method such as log analysis, pattern recognition, anomaly detection, and the like, but is not limited to such examples. For example, the generation AI proposes an optimal response method based on past fault data and a procedure manual. The provision unit provides a specific response method based on the result analyzed by the analysis unit. The specific response method includes, for example, rebooting, applying a patch, changing settings, and the like, but is not limited to such examples. For example, the provision unit specifically tells which procedure should be performed for a specific error message. The monitoring unit monitors the system status on a daily basis. The monitoring is performed, for example, by a method such as real-time monitoring, regular monitoring, and alert setting, but is not limited to such examples. For example, the monitoring unit collects system performance data and issues an alert when an abnormality is detected. As a result, the system according to the embodiment can consistently perform operations from input of the fault condition to analysis, provision of a response method, and monitoring of the system status. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis using a generation AI model that takes the fault condition as input and outputs an analysis result. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the provision unit can provide a response method using a generation AI model that takes the result analyzed by the analysis unit as input and outputs a specific response method. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can perform monitoring using a generation AI model that takes the performance data of the system as input and detects anomalies.As a result, the system according to the embodiment can consistently perform operations from inputting a fault condition to analyzing it, providing a response method, and monitoring the system status.

[0030] The reception unit inputs the failure status. The failure status includes, but is not limited to, for example, a system error, a network failure, and a hardware failure. The reception unit receives, for example, the failure status input by the user and transmits it to the analysis unit. Specifically, the reception unit provides an interface for the user to input, allowing the user to input details of the failure. The interface includes input fields such as text boxes, drop-down menus, and check boxes, and is designed to allow the user to input detailed information such as the type of failure, the time of occurrence, and the scope of impact. Furthermore, the reception unit has a function of automatically formatting the information input by the user and shaping the data so that the analysis unit can process it efficiently. For example, the text data input by the user can be analyzed using natural language processing technology to automatically classify the type and cause of the failure. In addition, the reception unit provides a history function that allows the user to refer to failure information input in the past, so that a quick response can be made if a similar failure recurs. As a result, the reception unit provides an environment in which the user can input failure information accurately and quickly, and the failure response process of the entire system can be smoothly carried out.

[0031] The analysis unit uses the generation AI to analyze the fault condition input by the reception unit. The analysis is performed by, for example, log analysis, pattern recognition, anomaly detection, etc., but is not limited to such examples. Specifically, the generation AI proposes an optimal response method based on past fault data and procedure manuals. The generation AI first analyzes the fault information received from the reception unit and identifies the type and cause of the fault. Next, it refers to a past fault database and analyzes the response method and results when a similar fault occurs. Furthermore, the generation AI analyzes the log data, identifies events and error messages recorded before and after the fault occurs, and identifies the root cause of the fault. For example, the generation AI can analyze system logs and network traffic data to detect abnormal patterns and error messages. In addition, the generation AI can use pattern recognition technology to extract common patterns and trends from past fault data and predict future faults and propose preventive measures. In addition, the generation AI can use an anomaly detection algorithm to detect abnormal behavior that deviates from normal system operation in real time and issue an early warning. This enables the analysis unit to quickly and accurately analyze the collected data, identify the cause of the failure, and propose the most appropriate response method.

[0032] The providing unit provides a specific response method based on the results of the analysis by the analyzing unit. Specific response methods include, for example, rebooting, applying a patch, changing settings, and the like, but are not limited to such examples. For example, the providing unit specifically tells the user which procedure to execute for a specific error message. The providing unit provides the user with a specific response procedure based on the analysis results received from the analyzing unit. For example, if a reboot is required for the failure the user is facing, the providing unit explains the reboot procedure in detail and provides the necessary commands and operation procedures. In addition, if a patch application is required, the providing unit provides a download link and installation procedure for the patch to be applied, and supports the user so that the user can apply the patch smoothly. Furthermore, if a setting change is required, the providing unit provides specific setting items and change procedures so that the user can change the settings while preventing erroneous operations. The providing unit also has a function of providing support in real time when the user executes the provided procedure. For example, if a question or problem occurs when the user executes the procedure, the providing unit responds quickly through a chatbot or online support to help the user solve the problem. In this way, the providing unit supports the user so that the user can respond quickly and appropriately to the failure, thereby improving the stability and reliability of the system.

[0033] The monitoring unit monitors the daily system status. The monitoring is performed by, for example, real-time monitoring, regular monitoring, alert setting, and the like, but is not limited to such examples. For example, the monitoring unit collects performance data of the system and issues an alert when an abnormality is detected. Specifically, the monitoring unit monitors performance data such as the system's CPU usage rate, memory usage rate, disk I / O, and network traffic in real time to detect abnormal behavior and performance degradation. The monitoring unit has a function of immediately issuing an alert and notifying the administrator when an abnormality is detected. The alert is sent by email, SMS, push notification, and the like, so that the administrator can respond quickly. The monitoring unit also has a function of periodically checking the system's health status and generating regular reports. The regular reports include the system's operating status, performance trends, past abnormality detection history, and the like, so that the administrator can comprehensively grasp the system's status. Furthermore, the monitoring unit can improve the accuracy of abnormality detection by using a generation AI. The generation AI can detect abnormal behavior with high accuracy by learning past performance data and modeling normal operation patterns. This enables the monitoring unit to maintain the stability and reliability of the system and assist in early detection of failures and rapid response.

[0034] The system includes a collection unit that collects a real-time system log. The collection unit collects the system log in real time. Specific time ranges and standards of real time include, for example, seconds, minutes, and immediate, but are not limited to such examples. For example, the collection unit collects system log data in real time and transmits it to the analysis unit. The collection unit can collect, for example, the system operation status and error logs in real time and provide them to the analysis unit. This allows for rapid failure response by collecting the system log in real time. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect logs using an AI model that inputs system log data and collects it in real time.

[0035] The system includes an evaluation unit that evaluates the effect of the proposed response method. The evaluation unit evaluates the effect of the proposed response method. Specific criteria and methods for evaluating the effect include, but are not limited to, for example, success rate, time reduction, and cost reduction. For example, the evaluation unit evaluates the effect of the response method proposed by the provision unit and provides the result to the feedback unit. The evaluation unit can, for example, monitor system performance after the implementation of the response method and evaluate the effect. In this way, the accuracy of the response can be improved by evaluating the effect of the proposed response method. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can perform the evaluation using an AI model that inputs the implementation result of the response method and evaluates the effect.

[0036] The system includes a feedback unit that forms a feedback loop. The feedback unit forms the feedback loop. Specific methods and standards for forming the feedback loop include, but are not limited to, periodic reviews and collection of user feedback. For example, the feedback unit identifies improvements to the system based on the evaluation results provided by the evaluation unit, and updates the corresponding method. The feedback unit can, for example, collect user feedback and improve the system based on the results. In this way, the feedback loop can be formed, thereby making it possible to continuously improve the system. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can form a feedback loop using an AI model that inputs the evaluation results and identifies improvements.

[0037] The collection unit can cooperate with the monitoring unit to collect system performance data. The collection unit cooperates with the monitoring unit to collect system performance data. Specific types and collection methods of performance data include, for example, CPU usage, memory usage, response time, etc., but are not limited to such examples. For example, the collection unit collects system performance data provided from the monitoring unit and provides it to the analysis unit. The collection unit can, for example, collect the CPU usage and memory usage of the system in real time and send it to the analysis unit. Thereby, by collecting performance data in cooperation with the monitoring unit, it is possible to grasp the state of the system more accurately. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that inputs system performance data and collects it in real time.

[0038] The evaluation unit can cooperate with the provision unit to evaluate the effect of the proposed response method. The evaluation unit cooperates with the provision unit to evaluate the effect of the proposed response method. Specific criteria and methods for evaluating the effect include, for example, success rate, time reduction, cost reduction, etc., but are not limited to such examples. For example, the evaluation unit evaluates the effect of the response method provided by the provision unit and provides the result to the feedback unit. The evaluation unit can, for example, monitor system performance after the implementation of the response method and evaluate the effect. As a result, the accuracy of the response can be improved by evaluating the effect of the response method in cooperation with the provision unit. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can perform the evaluation using an AI model that inputs the implementation result of the response method and evaluates the effect.

[0039] The reception unit can analyze the input history of the past failure situations and propose the optimal input method. The reception unit analyzes the input history of the past failure situations. Specific methods and standards for analyzing the input history include, for example, frequency analysis, pattern recognition, trend analysis, etc., but are not limited to such examples. For example, the reception unit automatically displays the failure situations that the user frequently inputs in the past as candidates. The reception unit can also preferentially propose the input method (voice, text, etc.) that the user used in the past. Furthermore, the reception unit can predict and propose a failure situation that will occur in a specific time period from the user's past input history. In this way, the optimal input method can be proposed to the user by analyzing the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past input history data to the generation AI and cause the generation AI to execute the proposal of the optimal input method.

[0040] The reception unit can automatically complete the input contents based on the user's current work situation when the failure situation is input. The reception unit automatically completes the input contents based on the user's current work situation when the failure situation is input. Specific methods and standards for automatic completion include, for example, a prediction algorithm, the use of past data, and the like, but are not limited to such examples. For example, when a failure occurs while the user is using a specific application, the reception unit automatically inputs the name of the application. The reception unit can also automatically complete the related failure situation based on the work content performed by the user in a specific time period. Furthermore, the reception unit can also simplify the input of the failure situation by referring to the work content performed by the user in the past. This makes it possible to reduce the effort of input by automatically completing the input contents based on the user's work situation. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's work situation data to the generation AI and cause the generation AI to automatically complete the input contents.

[0041] The reception unit can preferentially input information with high relevance based on the geographical location information of the user when inputting a fault situation. The reception unit preferentially inputs information with high relevance based on the geographical location information of the user when inputting a fault situation. Specific types and acquisition methods of geographical location information include, for example, GPS data, IP address, Wi-Fi location information, etc., but are not limited to such examples. For example, when a user inputs a fault situation that occurred in a specific area, the reception unit automatically complements information related to the area. In addition, when a user inputs a fault situation that occurred while moving, the reception unit can also preferentially input related information based on the current location. Furthermore, when a user inputs a fault situation that occurred in a specific facility, the reception unit can automatically complement information related to the facility. As a result, by inputting information with high relevance based on geographical location information, the accuracy of input can be improved. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute complementation of information with high relevance.

[0042] The reception unit can analyze the social media activity of the user and input related information when the failure situation is input. The reception unit can analyze the social media activity of the user and input related information when the failure situation is input. Specific types and analysis methods of social media activities include, for example, the content of posts, the number of likes, the number of comments, etc., but are not limited to such examples. For example, the reception unit automatically inputs the failure situation shared by the user on social media. The reception unit can also input related failure situations based on information of accounts the user follows on social media. Furthermore, the reception unit can analyze information posted by the user on social media and complement the input of the failure situation. In this way, the related information can be automatically input by analyzing the social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media data of the user to the generation AI and cause the generation AI to execute the input of related information.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the failure during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the failure during the analysis. Specific evaluation criteria and methods of the importance include, for example, the scope of impact, urgency, business impact, etc., but are not limited to such examples. For example, the analysis unit performs a detailed analysis in the case of a serious failure and provides a specific response method. In addition, the analysis unit can also perform a simplified analysis in the case of a minor failure and provide a basic response method. Furthermore, in the case of a medium failure, the analysis unit can perform an analysis with a moderate level of detail and provide an appropriate response method. As a result, an appropriate response method can be provided by adjusting the level of detail of the analysis based on the importance of the failure. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the failure to the generation AI and adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the fault during analysis. The analysis unit applies different analysis algorithms depending on the category of the fault during analysis. Specific types and classification methods of categories include, for example, error type, system component, and impact range, but are not limited to such examples. For example, the analysis unit applies a network analysis algorithm in the case of a network fault. In addition, the analysis unit can also apply a hardware analysis algorithm in the case of a hardware fault. Furthermore, the analysis unit can also apply a software analysis algorithm in the case of a software fault. Thereby, by applying an appropriate analysis algorithm depending on the category of the fault, the accuracy of the analysis can be improved. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input fault category data to the generation AI and apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the time of occurrence of the fault during the analysis. The analysis unit determines the priority of the analysis based on the time of occurrence of the fault during the analysis. Specific evaluation criteria and methods for the time of occurrence include, for example, time period, date, weekend / weekday, etc., but are not limited to such examples. For example, the analysis unit prioritizes analysis of a fault that has recently occurred. The analysis unit can also prioritize analysis of a fault that has occurred in the past but is unsolved. Furthermore, the analysis unit can also prioritize analysis of a fault that occurs periodically. This enables a rapid response by determining the priority of the analysis based on the time of occurrence of the fault. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data on the time of occurrence of the fault to the generation AI and determine the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the faults during analysis. The analysis unit adjusts the order of analysis based on the relevance of the faults during analysis. Specific evaluation criteria and methods for the relevance include, for example, a common cause, an impact range, and a correlation, but are not limited to such examples. For example, the analysis unit prioritizes analysis of faults with high relevance. The analysis unit can also postpone analysis of faults with low relevance. Furthermore, when multiple faults are related, the analysis unit can also analyze them in order of relevance. This allows efficient analysis by adjusting the order of analysis based on the relevance of the faults. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input fault relevance data to the generation AI and adjust the order of analysis.

[0047] The providing unit can adjust the level of detail of the provision based on the importance of the response method at the time of provision. The providing unit adjusts the level of detail of the provision based on the importance of the response method at the time of provision. Specific evaluation criteria and methods of importance include, for example, the scope of impact, urgency, business impact, etc., but are not limited to such examples. For example, the providing unit provides a detailed response method in the case of a serious failure. In addition, the providing unit can also provide a simplified response method in the case of a minor failure. Furthermore, the providing unit can also provide a response method with a moderate level of detail in the case of a moderate failure. Thereby, by adjusting the level of detail of the provision based on the importance of the response method, an appropriate response method can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance data of the response method to the generating AI and adjust the level of detail of the provision.

[0048] The providing unit can apply different providing algorithms according to the category of the response method at the time of providing. The providing unit applies different providing algorithms according to the category of the response method at the time of providing. Specific types and classification methods of categories include, for example, error type, system component, and impact range, but are not limited to such examples. For example, in the case of a network failure, the providing unit applies a network-compatible algorithm. In addition, in the case of a hardware failure, the providing unit can also apply a hardware-compatible algorithm. Furthermore, in the case of a software failure, the providing unit can also apply a software-compatible algorithm. Thereby, by applying an appropriate providing algorithm according to the category of the response method, the accuracy of the response can be improved. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input category data of the response method to the generating AI and apply an appropriate providing algorithm.

[0049] The providing unit can determine the priority of provision based on the occurrence time of the response method at the time of provision. The providing unit determines the priority of provision based on the occurrence time of the response method at the time of provision. Specific evaluation criteria and methods for the occurrence time include, for example, time period, date, weekend / weekday, etc., but are not limited to such examples. For example, the providing unit provides preferentially a response method for a failure that has recently occurred. The providing unit can also provide preferentially a response method for a failure that has occurred in the past but is unresolved. Furthermore, the providing unit can also provide preferentially a response method for a failure that occurs periodically. This enables a rapid response by determining the priority of provision based on the occurrence time of the response method. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the occurrence time data of the response method to the generating AI and determine the priority of provision.

[0050] The providing unit can adjust the order of provision based on the relevance of the response methods at the time of provision. The providing unit adjusts the order of provision based on the relevance of the response methods at the time of provision. Specific evaluation criteria and methods of relevance include, for example, common causes, scope of influence, correlation, etc., but are not limited to such examples. For example, the providing unit provides highly relevant response methods preferentially. In addition, the providing unit can also postpone less relevant response methods. Furthermore, when multiple response methods are related, the providing unit can also provide them in order of relevance. As a result, by adjusting the order of provision based on the relevance of the response methods, efficient response is possible. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance data of the response methods to the generating AI and adjust the order of provision.

[0051] The monitoring unit can adjust the level of detail of monitoring based on the importance of the system during monitoring. The monitoring unit adjusts the level of detail of monitoring based on the importance of the system during monitoring. Specific evaluation criteria and methods of importance include, for example, the scope of impact, urgency, business impact, etc., but are not limited to such examples. For example, the monitoring unit performs detailed monitoring in the case of an important system and provides specific data. In addition, the monitoring unit can also perform simple monitoring in the case of a minor system and provide basic data. Furthermore, the monitoring unit can perform monitoring with a moderate level of detail and provide appropriate data in the case of a medium system. As a result, appropriate monitoring can be performed by adjusting the level of detail of monitoring based on the importance of the system. Part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the importance data of the system to the generation AI and adjust the level of detail of monitoring.

[0052] The monitoring unit can apply different monitoring algorithms depending on the system category during monitoring. The monitoring unit applies different monitoring algorithms depending on the system category during monitoring. Specific types and classification methods of categories include, for example, error type, system component, and impact range, but are not limited to such examples. For example, in the case of a network system, the monitoring unit applies a network monitoring algorithm. In addition, in the case of a hardware system, the monitoring unit can also apply a hardware monitoring algorithm. Furthermore, in the case of a software system, the monitoring unit can also apply a software monitoring algorithm. Thereby, by applying an appropriate monitoring algorithm depending on the system category, the accuracy of monitoring can be improved. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input system category data to a generation AI and apply an appropriate monitoring algorithm.

[0053] The monitoring unit can determine the monitoring priority based on the occurrence time of the system during monitoring. The monitoring unit determines the monitoring priority based on the occurrence time of the system during monitoring. Specific evaluation criteria and methods for the occurrence time include, for example, time period, date, weekend / weekday, etc., but are not limited to such examples. For example, the monitoring unit preferentially displays the monitoring results of a system that has recently occurred. The monitoring unit can also preferentially display the monitoring results of a system that has occurred in the past but is unresolved. Furthermore, the monitoring unit can also preferentially display the monitoring results of a system that occurs periodically. This enables a rapid response by determining the monitoring priority based on the occurrence time of the system. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the occurrence time data of the system to the generation AI and determine the monitoring priority.

[0054] The monitoring unit can adjust the monitoring order based on the relevance of the systems during monitoring. The monitoring unit adjusts the monitoring order based on the relevance of the systems during monitoring. Specific evaluation criteria and methods for relevance include, for example, common causes, scope of influence, correlation, etc., but are not limited to such examples. For example, the monitoring unit preferentially displays the monitoring results of systems with high relevance. In addition, the monitoring unit can also postpone the monitoring results of systems with low relevance. Furthermore, when multiple systems are related, the monitoring unit can also display the monitoring results in order of relevance. This enables efficient monitoring by adjusting the monitoring order based on the relevance of the systems. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the relevance data of the systems to the generation AI and adjust the monitoring order.

[0055] The collection unit can adjust the level of detail of the collection based on the importance of the system at the time of collection. The collection unit adjusts the level of detail of the collection based on the importance of the system at the time of collection. Specific evaluation criteria and methods of importance include, for example, the scope of impact, urgency, business impact, etc., but are not limited to such examples. For example, the collection unit collects detailed logs in the case of an important system. In addition, the collection unit can also collect simple logs in the case of a minor system. Furthermore, the collection unit can also collect logs with a moderate level of detail in the case of a medium system. As a result, appropriate log collection can be performed by adjusting the level of detail of the collection based on the importance of the system. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the importance data of the system to the generation AI and adjust the level of detail of the collection.

[0056] The collection unit can determine the priority of collection based on the time of occurrence of the system at the time of collection. The collection unit determines the priority of collection based on the time of occurrence of the system at the time of collection. Specific evaluation criteria and methods for the time of occurrence include, for example, time period, date, weekend / weekday, etc., but are not limited to such examples. For example, the collection unit preferentially collects logs of a system that has recently occurred. The collection unit can also preferentially collect logs of a system that has occurred in the past but is unsolved. Furthermore, the collection unit can also preferentially collect logs of a system that occurs periodically. This enables rapid log collection by determining the priority of collection based on the time of occurrence of the system. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the data on the time of occurrence of the system to the generation AI and determine the priority of collection.

[0057] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the response method at the time of evaluation. The evaluation unit adjusts the level of detail of the evaluation based on the importance of the response method at the time of evaluation. Specific evaluation criteria and methods of importance include, for example, the scope of impact, urgency, business impact, etc., but are not limited to such examples. For example, the evaluation unit performs a detailed evaluation in the case of a serious response method and provides a specific result. In addition, the evaluation unit can perform a simplified evaluation in the case of a minor response method and provide a basic result. Furthermore, in the case of a medium response method, the evaluation unit can perform an evaluation with a moderate level of detail and provide an appropriate result. As a result, an appropriate evaluation can be performed by adjusting the level of detail of the evaluation based on the importance of the response method. Part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the importance data of the response method to the generation AI and adjust the level of detail of the evaluation.

[0058] The evaluation unit can determine the priority of the evaluation based on the occurrence time of the response method at the time of evaluation. The evaluation unit determines the priority of the evaluation based on the occurrence time of the response method at the time of evaluation. Specific evaluation criteria and methods of the occurrence time include, for example, time period, date, weekend / weekday, etc., but are not limited to such examples. For example, the evaluation unit prioritizes evaluation of a response method that has been recently implemented. In addition, the evaluation unit can also prioritize evaluation of a response method that has been implemented in the past but has not been evaluated. Furthermore, the evaluation unit can also prioritize evaluation of a response method that is implemented periodically. This enables rapid evaluation by determining the priority of the evaluation based on the occurrence time of the response method. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the occurrence time data of the response method to the generation AI and determine the priority of the evaluation.

[0059] The feedback unit can adjust the level of detail of the feedback based on the importance of the system at the time of feedback. The feedback unit adjusts the level of detail of the feedback based on the importance of the system at the time of feedback. Specific evaluation criteria and methods of importance include, for example, the scope of impact, urgency, business impact, etc., but are not limited to such examples. For example, the feedback unit provides detailed feedback in the case of an important system. In addition, the feedback unit can also provide simple feedback in the case of a minor system. Furthermore, the feedback unit can also provide feedback with a moderate level of detail in the case of a medium system. Thereby, by adjusting the level of detail of the feedback based on the importance of the system, appropriate feedback can be provided. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the importance data of the system to the generation AI and adjust the level of detail of the feedback.

[0060] The feedback unit may determine the priority of the feedback based on the time of occurrence of the system at the time of feedback. The feedback unit may determine the priority of the feedback based on the time of occurrence of the system at the time of feedback. Specific evaluation criteria and methods for the time of occurrence include, for example, time period, date, weekend / weekday, etc., but are not limited to such examples. For example, the feedback unit may provide feedback of a system that has recently occurred preferentially. The feedback unit may also provide feedback of a system that has occurred in the past but is unresolved preferentially. Furthermore, the feedback unit may also provide feedback of a system that occurs periodically preferentially. This enables rapid feedback by determining the priority of the feedback based on the time of occurrence of the system. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit may input data on the time of occurrence of the system to a generation AI and determine the priority of the feedback.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] When inputting a fault condition, the system can automatically complete the input content based on the user's current work status. For example, if a fault occurs while the user is using a specific application, the name of that application is automatically entered. Also, the system can automatically complete related fault conditions based on the work the user is doing during a specific time period. Furthermore, the system can simplify the input of fault conditions by referring to the work the user has done in the past. This can reduce the effort required for input by automatically completing the input content based on the user's work status.

[0063] When a fault condition is input, the system can preferentially input highly relevant information based on the user's geographical location information. For example, when a user inputs a fault condition that has occurred in a specific area, information related to that area is automatically completed. Also, when a user inputs a fault condition that has occurred while moving, related information can be preferentially input based on the user's current location. Furthermore, when a user inputs a fault condition that has occurred within a specific facility, information related to that facility can be automatically completed. This makes it possible to improve the accuracy of input by inputting highly relevant information based on geographical location information.

[0064] During analysis, the system can adjust the level of detail of the analysis based on the severity of the failure. For example, in the case of a serious failure, a detailed analysis is performed and a specific response method is provided. In addition, in the case of a minor failure, a simplified analysis can be performed and a basic response method can be provided. Furthermore, in the case of a medium level of failure, an analysis can be performed with a moderate level of detail and an appropriate response method can be provided. In this way, by adjusting the level of detail of the analysis based on the severity of the failure, an appropriate response method can be provided.

[0065] During analysis, the system can apply different analysis algorithms depending on the category of the failure. For example, in the case of a network failure, a network analysis algorithm can be applied. In addition, in the case of a hardware failure, a hardware analysis algorithm can be applied. Furthermore, in the case of a software failure, a software analysis algorithm can be applied. In this way, the accuracy of the analysis can be improved by applying an appropriate analysis algorithm depending on the category of the failure.

[0066] When providing the response method, the system can adjust the level of detail of the response method based on the importance of the response method. For example, in the case of a serious failure, a detailed response method can be provided. In addition, in the case of a minor failure, a simplified response method can be provided. Furthermore, in the case of a medium failure, a response method can be provided with a moderate level of detail. In this way, by adjusting the level of detail of the response method based on the importance of the response method, an appropriate response method can be provided.

[0067] When providing, the system can apply different provision algorithms depending on the category of the response method. For example, in the case of a network failure, a network-enabled algorithm is applied. In addition, in the case of a hardware failure, a hardware-enabled algorithm can also be applied. Furthermore, in the case of a software failure, a software-enabled algorithm can also be applied. In this way, by applying an appropriate provision algorithm depending on the category of the response method, the accuracy of the response can be improved.

[0068] The process flow of the first embodiment will be briefly described below.

[0069] Step 1: The reception unit inputs the failure status. The failure status includes system errors, network failures, hardware failures, etc. The reception unit receives the failure status input by the user and transmits it to the analysis unit. Step 2: The analysis unit uses the generation AI to analyze the fault condition entered by the reception unit. The analysis is performed using methods such as log analysis, pattern recognition, and anomaly detection. The generation AI proposes the optimal response method based on past fault data and procedure manuals. Step 3: The provider provides specific countermeasures based on the results of the analysis by the analyzer. Specific countermeasures include rebooting, applying patches, changing settings, etc. The provider specifically tells the user which steps to take for a specific error message. Step 4: The monitoring unit monitors the daily system status. Monitoring can be performed in real-time, periodically, or by setting alerts. The monitoring unit collects system performance data and issues an alert if an abnormality is detected.

[0070] (Example 2) The system according to the embodiment of the present invention is a system that provides specific responses to system failures. This system will tell you specific ways to respond by simply inputting the failure status. In addition, by inputting the daily system status, the system itself will talk to you about "things that should be addressed" as necessary. This mechanism can prevent the cause investigation from taking too long and the impact from expanding due to incorrect operation. Furthermore, it also leads to improved service quality, reduced operator load, and increased sales through external sales. For example, the failure status is input. For example, a situation in which a specific part of the system does not work or an error message is displayed is input. This information is input to the generation AI. Next, the generation AI analyzes the input failure status and provides specific ways to respond. The generation AI proposes the optimal way to respond based on past failure data and procedure manuals. For example, it will specifically tell you which procedure to execute for a specific error message. In addition, by inputting the daily system status, the generation AI will talk to you about "things that should be addressed" as necessary. For example, if the system performance is deteriorating, the generation AI will identify the cause and propose a way to respond. In this way, the operator can always grasp the system status and take appropriate measures. This mechanism can prevent the cause investigation from taking too long and the impact from expanding due to incorrect operation. For example, when a failure occurs, the generation AI can quickly suggest a response method, allowing the operator to respond quickly. In addition, by monitoring the system status daily, it is possible to detect early signs of a failure and respond in advance. Furthermore, this mechanism also contributes to improving service quality. For example, by always keeping the system performance at an optimal state, the quality of service for users is improved. It also reduces the burden on operators. By the generation AI suggesting a specific response method, operators can save the trouble of having to refer to the procedure manual. Finally, this mechanism can also lead to increased sales through external sales. For example, by providing this system to other companies, sales can be increased. The specific response methods and daily system status monitoring functions provided by the generation AI are useful to many companies and are likely to be in high demand.This allows the system to prevent the investigation into the cause from being prolonged or the scope of impact from expanding due to incorrect operation.

[0071] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit inputs a fault condition. The fault condition includes, for example, a system error, a network fault, a hardware failure, and the like, but is not limited to such examples. The reception unit receives, for example, a fault condition input by a user, and transmits it to the analysis unit. The analysis unit uses a generation AI to analyze the fault condition input by the reception unit. The analysis is performed, for example, by a method such as log analysis, pattern recognition, anomaly detection, and the like, but is not limited to such examples. For example, the generation AI proposes an optimal response method based on past fault data and a procedure manual. The provision unit provides a specific response method based on the result analyzed by the analysis unit. The specific response method includes, for example, rebooting, applying a patch, changing settings, and the like, but is not limited to such examples. For example, the provision unit specifically tells which procedure should be performed for a specific error message. The monitoring unit monitors the system status on a daily basis. The monitoring is performed, for example, by a method such as real-time monitoring, regular monitoring, and alert setting, but is not limited to such examples. For example, the monitoring unit collects system performance data and issues an alert when an abnormality is detected. As a result, the system according to the embodiment can consistently perform operations from input of the fault condition to analysis, provision of a response method, and monitoring of the system status. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis using a generation AI model that takes the fault condition as input and outputs an analysis result. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the provision unit can provide a response method using a generation AI model that takes the result analyzed by the analysis unit as input and outputs a specific response method. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can perform monitoring using a generation AI model that takes the performance data of the system as input and detects anomalies.As a result, the system according to the embodiment can consistently perform operations from inputting a fault condition to analyzing it, providing a response method, and monitoring the system status.

[0072] The reception unit inputs the failure status. The failure status includes, but is not limited to, for example, a system error, a network failure, and a hardware failure. The reception unit receives, for example, the failure status input by the user and transmits it to the analysis unit. Specifically, the reception unit provides an interface for the user to input, allowing the user to input details of the failure. The interface includes input fields such as text boxes, drop-down menus, and check boxes, and is designed to allow the user to input detailed information such as the type of failure, the time of occurrence, and the scope of impact. Furthermore, the reception unit has a function of automatically formatting the information input by the user and shaping the data so that the analysis unit can process it efficiently. For example, the text data input by the user can be analyzed using natural language processing technology to automatically classify the type and cause of the failure. In addition, the reception unit provides a history function that allows the user to refer to failure information input in the past, so that a quick response can be made if a similar failure recurs. As a result, the reception unit provides an environment in which the user can input failure information accurately and quickly, and the failure response process of the entire system can be smoothly carried out.

[0073] The analysis unit uses the generation AI to analyze the fault condition input by the reception unit. The analysis is performed by, for example, log analysis, pattern recognition, anomaly detection, etc., but is not limited to such examples. Specifically, the generation AI proposes an optimal response method based on past fault data and procedure manuals. The generation AI first analyzes the fault information received from the reception unit and identifies the type and cause of the fault. Next, it refers to a past fault database and analyzes the response method and results when a similar fault occurs. Furthermore, the generation AI analyzes the log data, identifies events and error messages recorded before and after the fault occurs, and identifies the root cause of the fault. For example, the generation AI can analyze system logs and network traffic data to detect abnormal patterns and error messages. In addition, the generation AI can use pattern recognition technology to extract common patterns and trends from past fault data and predict future faults and propose preventive measures. In addition, the generation AI can use an anomaly detection algorithm to detect abnormal behavior that deviates from normal system operation in real time and issue an early warning. This enables the analysis unit to quickly and accurately analyze the collected data, identify the cause of the failure, and propose the most appropriate response method.

[0074] The providing unit provides a specific response method based on the results of the analysis by the analyzing unit. Specific response methods include, for example, rebooting, applying a patch, changing settings, and the like, but are not limited to such examples. For example, the providing unit specifically tells the user which procedure to execute for a specific error message. The providing unit provides the user with a specific response procedure based on the analysis results received from the analyzing unit. For example, if a reboot is required for the failure the user is facing, the providing unit explains the reboot procedure in detail and provides the necessary commands and operation procedures. In addition, if a patch application is required, the providing unit provides a download link and installation procedure for the patch to be applied, and supports the user so that the user can apply the patch smoothly. Furthermore, if a setting change is required, the providing unit provides specific setting items and change procedures so that the user can change the settings while preventing erroneous operations. The providing unit also has a function of providing support in real time when the user executes the provided procedure. For example, if a question or problem occurs when the user executes the procedure, the providing unit responds quickly through a chatbot or online support to help the user solve the problem. In this way, the providing unit supports the user so that the user can respond quickly and appropriately to the failure, thereby improving the stability and reliability of the system.

[0075] The monitoring unit monitors the daily system status. The monitoring is performed by, for example, real-time monitoring, regular monitoring, alert setting, and the like, but is not limited to such examples. For example, the monitoring unit collects performance data of the system and issues an alert when an abnormality is detected. Specifically, the monitoring unit monitors performance data such as the system's CPU usage rate, memory usage rate, disk I / O, and network traffic in real time to detect abnormal behavior and performance degradation. The monitoring unit has a function of immediately issuing an alert and notifying the administrator when an abnormality is detected. The alert is sent by email, SMS, push notification, and the like, so that the administrator can respond quickly. The monitoring unit also has a function of periodically checking the system's health status and generating regular reports. The regular reports include the system's operating status, performance trends, past abnormality detection history, and the like, so that the administrator can comprehensively grasp the system's status. Furthermore, the monitoring unit can improve the accuracy of abnormality detection by using a generation AI. The generation AI can detect abnormal behavior with high accuracy by learning past performance data and modeling normal operation patterns. This enables the monitoring unit to maintain the stability and reliability of the system and assist in early detection of failures and rapid response.

[0076] The system includes a collection unit that collects a real-time system log. The collection unit collects the system log in real time. Specific time ranges and standards of real time include, for example, seconds, minutes, and immediate, but are not limited to such examples. For example, the collection unit collects system log data in real time and transmits it to the analysis unit. The collection unit can collect, for example, the system operation status and error logs in real time and provide them to the analysis unit. This allows for rapid failure response by collecting the system log in real time. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect logs using an AI model that inputs system log data and collects it in real time.

[0077] The system includes an evaluation unit that evaluates the effect of the proposed response method. The evaluation unit evaluates the effect of the proposed response method. Specific criteria and methods for evaluating the effect include, but are not limited to, for example, success rate, time reduction, and cost reduction. For example, the evaluation unit evaluates the effect of the response method proposed by the provision unit and provides the result to the feedback unit. The evaluation unit can, for example, monitor system performance after the implementation of the response method and evaluate the effect. In this way, the accuracy of the response can be improved by evaluating the effect of the proposed response method. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can perform the evaluation using an AI model that inputs the implementation result of the response method and evaluates the effect.

[0078] The system includes a feedback unit that forms a feedback loop. The feedback unit forms the feedback loop. Specific methods and standards for forming the feedback loop include, but are not limited to, periodic reviews and collection of user feedback. For example, the feedback unit identifies improvements to the system based on the evaluation results provided by the evaluation unit, and updates the corresponding method. The feedback unit can, for example, collect user feedback and improve the system based on the results. In this way, the feedback loop can be formed, thereby making it possible to continuously improve the system. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can form a feedback loop using an AI model that inputs the evaluation results and identifies improvements.

[0079] The collection unit can cooperate with the monitoring unit to collect system performance data. The collection unit cooperates with the monitoring unit to collect system performance data. Specific types and collection methods of performance data include, for example, CPU usage, memory usage, response time, etc., but are not limited to such examples. For example, the collection unit collects system performance data provided from the monitoring unit and provides it to the analysis unit. The collection unit can, for example, collect the CPU usage and memory usage of the system in real time and send it to the analysis unit. Thereby, by collecting performance data in cooperation with the monitoring unit, it is possible to grasp the state of the system more accurately. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that inputs system performance data and collects it in real time.

[0080] The evaluation unit can cooperate with the provision unit to evaluate the effect of the proposed response method. The evaluation unit cooperates with the provision unit to evaluate the effect of the proposed response method. Specific criteria and methods for evaluating the effect include, for example, success rate, time reduction, cost reduction, etc., but are not limited to such examples. For example, the evaluation unit evaluates the effect of the response method provided by the provision unit and provides the result to the feedback unit. The evaluation unit can, for example, monitor system performance after the implementation of the response method and evaluate the effect. As a result, the accuracy of the response can be improved by evaluating the effect of the response method in cooperation with the provision unit. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can perform the evaluation using an AI model that inputs the implementation result of the response method and evaluates the effect.

[0081] The reception unit can estimate the user's emotion and adjust the input interface for the obstacle status based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple interface and minimizes the input procedure. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the obstacle status to be input quickly. This can reduce the burden on the user by adjusting the input interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0082] The reception unit can analyze the input history of the past failure situations and propose the optimal input method. The reception unit analyzes the input history of the past failure situations. Specific methods and standards for analyzing the input history include, for example, frequency analysis, pattern recognition, trend analysis, etc., but are not limited to such examples. For example, the reception unit automatically displays the failure situations that the user frequently inputs in the past as candidates. The reception unit can also preferentially propose the input method (voice, text, etc.) that the user used in the past. Furthermore, the reception unit can predict and propose a failure situation that will occur in a specific time period from the user's past input history. In this way, the optimal input method can be proposed to the user by analyzing the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past input history data to the generation AI and cause the generation AI to execute the proposal of the optimal input method.

[0083] The reception unit can automatically complete the input contents based on the user's current work situation when the failure situation is input. The reception unit automatically completes the input contents based on the user's current work situation when the failure situation is input. Specific methods and standards for automatic completion include, for example, a prediction algorithm, the use of past data, and the like, but are not limited to such examples. For example, when a failure occurs while the user is using a specific application, the reception unit automatically inputs the name of the application. The reception unit can also automatically complete the related failure situation based on the work content performed by the user in a specific time period. Furthermore, the reception unit can also simplify the input of the failure situation by referring to the work content performed by the user in the past. This makes it possible to reduce the effort of input by automatically completing the input contents based on the user's work situation. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's work situation data to the generation AI and cause the generation AI to automatically complete the input contents.

[0084] The reception unit can estimate the user's emotion and determine the priority of the inputted trouble situation based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the priority of the inputted trouble situation based on the estimated user's emotion. Specific methods and standards for estimating the emotion include, but are not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, when the user is nervous, the reception unit preferentially processes a trouble situation with a high importance. In addition, when the user is relaxed, the reception unit can also process a trouble situation with a normal priority. Furthermore, when the user is in a hurry, the reception unit can also preferentially process a trouble situation that requires a quick response. In this way, by determining the priority of the trouble situation based on the user's emotion, it is possible to quickly respond to an important trouble. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0085] The reception unit can preferentially input information with high relevance based on the geographical location information of the user when inputting a fault situation. The reception unit preferentially inputs information with high relevance based on the geographical location information of the user when inputting a fault situation. Specific types and acquisition methods of geographical location information include, for example, GPS data, IP address, Wi-Fi location information, etc., but are not limited to such examples. For example, when a user inputs a fault situation that occurred in a specific area, the reception unit automatically complements information related to the area. In addition, when a user inputs a fault situation that occurred while moving, the reception unit can also preferentially input related information based on the current location. Furthermore, when a user inputs a fault situation that occurred in a specific facility, the reception unit can automatically complement information related to the facility. As a result, by inputting information with high relevance based on geographical location information, the accuracy of input can be improved. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute complementation of information with high relevance.

[0086] The reception unit can analyze the social media activity of the user and input related information when the failure situation is input. The reception unit can analyze the social media activity of the user and input related information when the failure situation is input. Specific types and analysis methods of social media activities include, for example, the content of posts, the number of likes, the number of comments, etc., but are not limited to such examples. For example, the reception unit automatically inputs the failure situation shared by the user on social media. The reception unit can also input related failure situations based on information of accounts the user follows on social media. Furthermore, the reception unit can analyze information posted by the user on social media and complement the input of the failure situation. In this way, the related information can be automatically input by analyzing the social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media data of the user to the generation AI and cause the generation AI to execute the input of related information.

[0087] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis result based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the expression method of the analysis result based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. For example, the analysis unit provides a simple and highly visible analysis result when the user is nervous. In addition, the analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide an analysis result that covers the main points when the user is in a hurry. In this way, by adjusting the expression method of the analysis result according to the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input a user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the failure during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the failure during the analysis. Specific evaluation criteria and methods of the importance include, for example, the scope of impact, urgency, business impact, etc., but are not limited to such examples. For example, the analysis unit performs a detailed analysis in the case of a serious failure and provides a specific response method. In addition, the analysis unit can also perform a simplified analysis in the case of a minor failure and provide a basic response method. Furthermore, in the case of a medium failure, the analysis unit can perform an analysis with a moderate level of detail and provide an appropriate response method. As a result, an appropriate response method can be provided by adjusting the level of detail of the analysis based on the importance of the failure. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the failure to the generation AI and adjust the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the category of the fault during analysis. The analysis unit applies different analysis algorithms depending on the category of the fault during analysis. Specific types and classification methods of categories include, for example, error type, system component, and impact range, but are not limited to such examples. For example, the analysis unit applies a network analysis algorithm in the case of a network fault. In addition, the analysis unit can also apply a hardware analysis algorithm in the case of a hardware fault. Furthermore, the analysis unit can also apply a software analysis algorithm in the case of a software fault. Thereby, by applying an appropriate analysis algorithm depending on the category of the fault, the accuracy of the analysis can be improved. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input fault category data to the generation AI and apply an appropriate analysis algorithm.

[0090] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. The analysis unit estimates the user's emotion and determines the priority of the analysis results based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. For example, when the user is nervous, the analysis unit preferentially displays analysis results with high importance. In addition, when the user is relaxed, the analysis unit can also display analysis results with normal priority. Furthermore, when the user is in a hurry, the analysis unit can also preferentially display analysis results that require a quick response. In this way, by determining the priority of the analysis results based on the user's emotion, important analysis results can be provided quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input a user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0091] The analysis unit can determine the priority of the analysis based on the time of occurrence of the fault during the analysis. The analysis unit determines the priority of the analysis based on the time of occurrence of the fault during the analysis. Specific evaluation criteria and methods for the time of occurrence include, for example, time period, date, weekend / weekday, etc., but are not limited to such examples. For example, the analysis unit prioritizes analysis of a fault that has recently occurred. The analysis unit can also prioritize analysis of a fault that has occurred in the past but is unsolved. Furthermore, the analysis unit can also prioritize analysis of a fault that occurs periodically. This enables a rapid response by determining the priority of the analysis based on the time of occurrence of the fault. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data on the time of occurrence of the fault to the generation AI and determine the priority of the analysis.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the faults during analysis. The analysis unit adjusts the order of analysis based on the relevance of the faults during analysis. Specific evaluation criteria and methods for the relevance include, for example, a common cause, an impact range, and a correlation, but are not limited to such examples. For example, the analysis unit prioritizes analysis of faults with high relevance. The analysis unit can also postpone analysis of faults with low relevance. Furthermore, when multiple faults are related, the analysis unit can also analyze them in order of relevance. This allows efficient analysis by adjusting the order of analysis based on the relevance of the faults. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input fault relevance data to the generation AI and adjust the order of analysis.

[0093] The providing unit can estimate the user's emotion and adjust the expression method of the response method to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the expression method of the response method to be provided based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. For example, when the user is nervous, the providing unit provides a simple and highly visible response method. In addition, when the user is relaxed, the providing unit can also provide a detailed response method. Furthermore, when the user is in a hurry, the providing unit can also provide a response method that covers the main points. In this way, by adjusting the expression method of the response method according to the user's emotion, it is possible to provide a response method that is easy for the user to understand. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's facial expression data to the generating AI and cause the generating AI to estimate emotions.

[0094] The providing unit can adjust the level of detail of the provision based on the importance of the response method at the time of provision. The providing unit adjusts the level of detail of the provision based on the importance of the response method at the time of provision. Specific evaluation criteria and methods of importance include, for example, the scope of impact, urgency, business impact, etc., but are not limited to such examples. For example, the providing unit provides a detailed response method in the case of a serious failure. In addition, the providing unit can also provide a simplified response method in the case of a minor failure. Furthermore, the providing unit can also provide a response method with a moderate level of detail in the case of a moderate failure. Thereby, by adjusting the level of detail of the provision based on the importance of the response method, an appropriate response method can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance data of the response method to the generating AI and adjust the level of detail of the provision.

[0095] The providing unit can apply different providing algorithms according to the category of the response method at the time of providing. The providing unit applies different providing algorithms according to the category of the response method at the time of providing. Specific types and classification methods of categories include, for example, error type, system component, and impact range, but are not limited to such examples. For example, in the case of a network failure, the providing unit applies a network-compatible algorithm. In addition, in the case of a hardware failure, the providing unit can also apply a hardware-compatible algorithm. Furthermore, in the case of a software failure, the providing unit can also apply a software-compatible algorithm. Thereby, by applying an appropriate providing algorithm according to the category of the response method, the accuracy of the response can be improved. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input category data of the response method to the generating AI and apply an appropriate providing algorithm.

[0096] The providing unit can estimate the user's emotion and determine the priority order of the response methods to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and determines the priority order of the response methods to be provided based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, and the like, but are not limited to such examples. For example, when the user is nervous, the providing unit provides a response method with a high importance with priority. In addition, when the user is relaxed, the providing unit can also provide a response method with normal priority. Furthermore, when the user is in a hurry, the providing unit can also provide a response method that requires a quick response with priority. In this way, by determining the priority order of the response methods based on the user's emotion, it is possible to quickly provide an important response method. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's facial expression data to the generating AI and cause the generating AI to estimate emotions.

[0097] The providing unit can determine the priority of provision based on the occurrence time of the response method at the time of provision. The providing unit determines the priority of provision based on the occurrence time of the response method at the time of provision. Specific evaluation criteria and methods for the occurrence time include, for example, time period, date, weekend / weekday, etc., but are not limited to such examples. For example, the providing unit provides preferentially a response method for a failure that has recently occurred. The providing unit can also provide preferentially a response method for a failure that has occurred in the past but is unresolved. Furthermore, the providing unit can also provide preferentially a response method for a failure that occurs periodically. This enables a rapid response by determining the priority of provision based on the occurrence time of the response method. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the occurrence time data of the response method to the generating AI and determine the priority of provision.

[0098] The providing unit can adjust the order of provision based on the relevance of the response methods at the time of provision. The providing unit adjusts the order of provision based on the relevance of the response methods at the time of provision. Specific evaluation criteria and methods of relevance include, for example, common causes, scope of influence, correlation, etc., but are not limited to such examples. For example, the providing unit provides highly relevant response methods preferentially. In addition, the providing unit can also postpone less relevant response methods. Furthermore, when multiple response methods are related, the providing unit can also provide them in order of relevance. As a result, by adjusting the order of provision based on the relevance of the response methods, efficient response is possible. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance data of the response methods to the generating AI and adjust the order of provision.

[0099] The monitoring unit can estimate the user's emotion and adjust the display method of the monitoring result based on the estimated user's emotion. The monitoring unit estimates the user's emotion and adjusts the display method of the monitoring result based on the estimated user's emotion. Specific methods and standards for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, when the user is nervous, the monitoring unit provides a simple and highly visible monitoring result. In addition, when the user is relaxed, the monitoring unit can also provide a detailed monitoring result. Furthermore, when the user is in a hurry, the monitoring unit can also provide a monitoring result that covers the main points. In this way, by adjusting the display method of the monitoring result according to the user's emotion, it is possible to provide a monitoring result that is easy for the user to understand. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0100] The monitoring unit can adjust the level of detail of monitoring based on the importance of the system during monitoring. The monitoring unit adjusts the level of detail of monitoring based on the importance of the system during monitoring. Specific evaluation criteria and methods of importance include, for example, the scope of impact, urgency, business impact, etc., but are not limited to such examples. For example, the monitoring unit performs detailed monitoring in the case of an important system and provides specific data. In addition, the monitoring unit can also perform simple monitoring in the case of a minor system and provide basic data. Furthermore, the monitoring unit can perform monitoring with a moderate level of detail and provide appropriate data in the case of a medium system. As a result, appropriate monitoring can be performed by adjusting the level of detail of monitoring based on the importance of the system. Part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the importance data of the system to the generation AI and adjust the level of detail of monitoring.

[0101] The monitoring unit can apply different monitoring algorithms depending on the system category during monitoring. The monitoring unit applies different monitoring algorithms depending on the system category during monitoring. Specific types and classification methods of categories include, for example, error type, system component, and impact range, but are not limited to such examples. For example, in the case of a network system, the monitoring unit applies a network monitoring algorithm. In addition, in the case of a hardware system, the monitoring unit can also apply a hardware monitoring algorithm. Furthermore, in the case of a software system, the monitoring unit can also apply a software monitoring algorithm. Thereby, by applying an appropriate monitoring algorithm depending on the system category, the accuracy of monitoring can be improved. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input system category data to a generation AI and apply an appropriate monitoring algorithm.

[0102] The monitoring unit can estimate the user's emotion and determine the priority of the monitoring results based on the estimated user's emotion. The monitoring unit estimates the user's emotion and determines the priority of the monitoring results based on the estimated user's emotion. Specific methods and standards for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, when the user is nervous, the monitoring unit preferentially displays monitoring results with high importance. In addition, when the user is relaxed, the monitoring unit can also display monitoring results with normal priority. Furthermore, when the user is in a hurry, the monitoring unit can also preferentially display monitoring results that require a quick response. In this way, by determining the priority of the monitoring results based on the user's emotion, important monitoring results can be provided quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0103] The monitoring unit can determine the monitoring priority based on the occurrence time of the system during monitoring. The monitoring unit determines the monitoring priority based on the occurrence time of the system during monitoring. Specific evaluation criteria and methods for the occurrence time include, for example, time period, date, weekend / weekday, etc., but are not limited to such examples. For example, the monitoring unit preferentially displays the monitoring results of a system that has recently occurred. The monitoring unit can also preferentially display the monitoring results of a system that has occurred in the past but is unresolved. Furthermore, the monitoring unit can also preferentially display the monitoring results of a system that occurs periodically. This enables a rapid response by determining the monitoring priority based on the occurrence time of the system. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the occurrence time data of the system to the generation AI and determine the monitoring priority.

[0104] The monitoring unit can adjust the monitoring order based on the relevance of the systems during monitoring. The monitoring unit adjusts the monitoring order based on the relevance of the systems during monitoring. Specific evaluation criteria and methods for relevance include, for example, common causes, scope of influence, correlation, etc., but are not limited to such examples. For example, the monitoring unit preferentially displays the monitoring results of systems with high relevance. In addition, the monitoring unit can also postpone the monitoring results of systems with low relevance. Furthermore, when multiple systems are related, the monitoring unit can also display the monitoring results in order of relevance. This enables efficient monitoring by adjusting the monitoring order based on the relevance of the systems. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the relevance data of the systems to the generation AI and adjust the monitoring order.

[0105] The collection unit can estimate the user's emotion and determine the priority of logs to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion and determines the priority of logs to be collected based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, and the like, but are not limited to such examples. For example, when the user is nervous, the collection unit preferentially collects logs with high importance. In addition, when the user is relaxed, the collection unit can also collect logs with normal priority. Furthermore, when the user is in a hurry, the collection unit can also preferentially collect logs that require a quick response. In this way, important logs can be quickly collected by determining the priority of logs based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0106] The collection unit can adjust the level of detail of the collection based on the importance of the system at the time of collection. The collection unit adjusts the level of detail of the collection based on the importance of the system at the time of collection. Specific evaluation criteria and methods of importance include, for example, the scope of impact, urgency, business impact, etc., but are not limited to such examples. For example, the collection unit collects detailed logs in the case of an important system. In addition, the collection unit can also collect simple logs in the case of a minor system. Furthermore, the collection unit can also collect logs with a moderate level of detail in the case of a medium system. As a result, appropriate log collection can be performed by adjusting the level of detail of the collection based on the importance of the system. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the importance data of the system to the generation AI and adjust the level of detail of the collection.

[0107] The collection unit can estimate the user's emotion and adjust the display method of the collected log based on the estimated user's emotion. The collection unit estimates the user's emotion and adjusts the display method of the collected log based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. For example, the collection unit provides a simple and highly visible log display when the user is nervous. In addition, the collection unit can also provide a detailed log display when the user is relaxed. Furthermore, the collection unit can also provide a log display that focuses on the main points when the user is in a hurry. In this way, by adjusting the display method of the log according to the user's emotion, it is possible to provide a log display that is easy for the user to understand. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0108] The collection unit can determine the priority of collection based on the time of occurrence of the system at the time of collection. The collection unit determines the priority of collection based on the time of occurrence of the system at the time of collection. Specific evaluation criteria and methods for the time of occurrence include, for example, time period, date, weekend / weekday, etc., but are not limited to such examples. For example, the collection unit preferentially collects logs of a system that has recently occurred. The collection unit can also preferentially collect logs of a system that has occurred in the past but is unsolved. Furthermore, the collection unit can also preferentially collect logs of a system that occurs periodically. This enables rapid log collection by determining the priority of collection based on the time of occurrence of the system. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the data on the time of occurrence of the system to the generation AI and determine the priority of collection.

[0109] The evaluation unit can estimate the user's emotion and adjust the display method of the evaluation result based on the estimated user's emotion. The evaluation unit estimates the user's emotion and adjusts the display method of the evaluation result based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. For example, the evaluation unit provides a simple and highly visible evaluation result when the user is nervous. In addition, the evaluation unit can also provide a detailed evaluation result when the user is relaxed. Furthermore, the evaluation unit can also provide an evaluation result that covers the main points when the user is in a hurry. As a result, by adjusting the display method of the evaluation result according to the user's emotion, it is possible to provide an evaluation result that is easy for the user to understand. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evaluation unit can input a user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0110] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the response method at the time of evaluation. The evaluation unit adjusts the level of detail of the evaluation based on the importance of the response method at the time of evaluation. Specific evaluation criteria and methods of importance include, for example, the scope of impact, urgency, business impact, etc., but are not limited to such examples. For example, the evaluation unit performs a detailed evaluation in the case of a serious response method and provides a specific result. In addition, the evaluation unit can perform a simplified evaluation in the case of a minor response method and provide a basic result. Furthermore, in the case of a medium response method, the evaluation unit can perform an evaluation with a moderate level of detail and provide an appropriate result. As a result, an appropriate evaluation can be performed by adjusting the level of detail of the evaluation based on the importance of the response method. Part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the importance data of the response method to the generation AI and adjust the level of detail of the evaluation.

[0111] The evaluation unit can estimate the user's emotion and determine the priority order of the evaluation results based on the estimated user's emotion. The evaluation unit estimates the user's emotion and determines the priority order of the evaluation results based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, and the like, but are not limited to such examples. For example, when the user is nervous, the evaluation unit preferentially displays evaluation results with high importance. In addition, when the user is relaxed, the evaluation unit can also display evaluation results with normal priority. Furthermore, when the user is in a hurry, the evaluation unit can also preferentially display evaluation results that require a quick response. In this way, by determining the priority order of the evaluation results based on the user's emotion, important evaluation results can be provided quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input a user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0112] The evaluation unit can determine the priority of the evaluation based on the occurrence time of the response method at the time of evaluation. The evaluation unit determines the priority of the evaluation based on the occurrence time of the response method at the time of evaluation. Specific evaluation criteria and methods of the occurrence time include, for example, time period, date, weekend / weekday, etc., but are not limited to such examples. For example, the evaluation unit prioritizes evaluation of a response method that has been recently implemented. In addition, the evaluation unit can also prioritize evaluation of a response method that has been implemented in the past but has not been evaluated. Furthermore, the evaluation unit can also prioritize evaluation of a response method that is implemented periodically. This enables rapid evaluation by determining the priority of the evaluation based on the occurrence time of the response method. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the occurrence time data of the response method to the generation AI and determine the priority of the evaluation.

[0113] The feedback unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. The feedback unit estimates the user's emotion and adjusts the content of the feedback based on the estimated user's emotion. Specific methods and standards for estimating the emotion include, but are not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, the feedback unit provides simple and highly visible feedback when the user is nervous. The feedback unit can also provide detailed feedback when the user is relaxed. Furthermore, the feedback unit can also provide feedback that focuses on the main points when the user is in a hurry. This allows the content of the feedback to be adjusted according to the user's emotion, thereby providing feedback that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0114] The feedback unit can adjust the level of detail of the feedback based on the importance of the system at the time of feedback. The feedback unit adjusts the level of detail of the feedback based on the importance of the system at the time of feedback. Specific evaluation criteria and methods of importance include, for example, the scope of impact, urgency, business impact, etc., but are not limited to such examples. For example, the feedback unit provides detailed feedback in the case of an important system. In addition, the feedback unit can also provide simple feedback in the case of a minor system. Furthermore, the feedback unit can also provide feedback with a moderate level of detail in the case of a medium system. Thereby, by adjusting the level of detail of the feedback based on the importance of the system, appropriate feedback can be provided. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the importance data of the system to the generation AI and adjust the level of detail of the feedback.

[0115] The feedback unit can estimate the user's emotion and determine the priority of the feedback based on the estimated user's emotion. The feedback unit estimates the user's emotion and determines the priority of the feedback based on the estimated user's emotion. Specific methods and standards for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, text analysis, and the like. For example, when the user is nervous, the feedback unit provides feedback with a high level of importance with priority. In addition, when the user is relaxed, the feedback unit can also provide feedback with normal priority. Furthermore, when the user is in a hurry, the feedback unit can also provide feedback that requires a quick response with priority. In this way, by determining the priority of the feedback based on the user's emotion, important feedback can be provided quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0116] The feedback unit may determine the priority of the feedback based on the time of occurrence of the system at the time of feedback. The feedback unit may determine the priority of the feedback based on the time of occurrence of the system at the time of feedback. Specific evaluation criteria and methods for the time of occurrence include, for example, time period, date, weekend / weekday, etc., but are not limited to such examples. For example, the feedback unit may provide feedback of a system that has recently occurred preferentially. The feedback unit may also provide feedback of a system that has occurred in the past but is unresolved preferentially. Furthermore, the feedback unit may also provide feedback of a system that occurs periodically preferentially. This enables rapid feedback by determining the priority of the feedback based on the time of occurrence of the system. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit may input data on the time of occurrence of the system to a generation AI and determine the priority of the feedback.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] The system can estimate the user's emotions and adjust the input interface for the disability situation based on the estimated emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Also, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly input the disability situation. In this way, the input interface can be adjusted according to the user's emotions, reducing the burden on the user.

[0119] When inputting a fault condition, the system can automatically complete the input content based on the user's current work status. For example, if a fault occurs while the user is using a specific application, the name of that application is automatically entered. Also, the system can automatically complete related fault conditions based on the work the user is doing during a specific time period. Furthermore, the system can simplify the input of fault conditions by referring to the work the user has done in the past. This can reduce the effort required for input by automatically completing the input content based on the user's work status.

[0120] The system can estimate the user's emotions and determine the priority of the inputted trouble situations based on the estimated emotions. For example, if the user is nervous, trouble situations with high importance are processed with priority. Also, if the user is relaxed, trouble situations can be processed with normal priority. Furthermore, if the user is in a hurry, trouble situations that require a quick response can be processed with priority. In this way, by determining the priority of trouble situations based on the user's emotions, it is possible to quickly respond to important troubles.

[0121] When a fault condition is input, the system can preferentially input highly relevant information based on the user's geographical location information. For example, when a user inputs a fault condition that has occurred in a specific area, information related to that area is automatically completed. Also, when a user inputs a fault condition that has occurred while moving, related information can be preferentially input based on the user's current location. Furthermore, when a user inputs a fault condition that has occurred within a specific facility, information related to that facility can be automatically completed. This makes it possible to improve the accuracy of input by inputting highly relevant information based on geographical location information.

[0122] The system can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated emotions. For example, if the user is nervous, a simple and highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, an analysis result that focuses on the main points can be provided. In this way, by adjusting the way in which the analysis results are presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0123] During analysis, the system can adjust the level of detail of the analysis based on the severity of the failure. For example, in the case of a serious failure, a detailed analysis is performed and a specific response method is provided. In addition, in the case of a minor failure, a simplified analysis can be performed and a basic response method can be provided. Furthermore, in the case of a medium level of failure, an analysis can be performed with a moderate level of detail and an appropriate response method can be provided. In this way, by adjusting the level of detail of the analysis based on the severity of the failure, an appropriate response method can be provided.

[0124] During analysis, the system can apply different analysis algorithms depending on the category of the failure. For example, in the case of a network failure, a network analysis algorithm can be applied. In addition, in the case of a hardware failure, a hardware analysis algorithm can be applied. Furthermore, in the case of a software failure, a software analysis algorithm can be applied. In this way, the accuracy of the analysis can be improved by applying an appropriate analysis algorithm depending on the category of the failure.

[0125] The system can estimate the user's emotions and adjust the way in which the response method is expressed based on the estimated emotions. For example, if the user is nervous, a simple and highly visible response method can be provided. If the user is relaxed, a detailed response method can be provided. Furthermore, if the user is in a hurry, a response method that focuses on the main points can be provided. In this way, by adjusting the way in which the response method is expressed according to the user's emotions, it is possible to provide a response method that is easy for the user to understand.

[0126] When providing the response method, the system can adjust the level of detail of the response method based on the importance of the response method. For example, in the case of a serious failure, a detailed response method can be provided. In addition, in the case of a minor failure, a simplified response method can be provided. Furthermore, in the case of a medium failure, a response method can be provided with a moderate level of detail. In this way, by adjusting the level of detail of the response method based on the importance of the response method, an appropriate response method can be provided.

[0127] When providing, the system can apply different provision algorithms depending on the category of the response method. For example, in the case of a network failure, a network-enabled algorithm is applied. In addition, in the case of a hardware failure, a hardware-enabled algorithm can also be applied. Furthermore, in the case of a software failure, a software-enabled algorithm can also be applied. In this way, by applying an appropriate provision algorithm depending on the category of the response method, the accuracy of the response can be improved.

[0128] The process flow of the second embodiment will be briefly described below.

[0129] Step 1: The reception unit inputs the failure status. The failure status includes system errors, network failures, hardware failures, etc. The reception unit receives the failure status input by the user and transmits it to the analysis unit. Step 2: The analysis unit uses the generation AI to analyze the fault condition entered by the reception unit. The analysis is performed using methods such as log analysis, pattern recognition, and anomaly detection. The generation AI proposes the optimal response method based on past fault data and procedure manuals. Step 3: The provider provides specific countermeasures based on the results of the analysis by the analyzer. Specific countermeasures include rebooting, applying patches, changing settings, etc. The provider specifically tells the user which steps to take for a specific error message. Step 4: The monitoring unit monitors the daily system status. Monitoring can be performed in real-time, periodically, or by setting alerts. The monitoring unit collects system performance data and issues an alert if an abnormality is detected.

[0130] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0131] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0132] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, monitoring unit, collection unit, evaluation unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and receives the fault condition input by the user and transmits it to the analysis unit. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the fault condition using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides a specific response method based on the analysis result. The monitoring unit is realized, for example, by the control unit 46A of the smart device 14, and monitors the daily system state. The collection unit is realized, for example, by the control unit 46A of the smart device 14, and collects system logs in real time. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the effect of the proposed response method. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and forms a feedback loop based on the evaluation result. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0137] 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.

[0138] 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 the voice according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0140] 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 performed in a secure state.

[0141] Fig. 4 shows an example of 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.

[0142] 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.

[0143] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0144] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0145] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0146] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0147] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0148] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, monitoring unit, collection unit, evaluation unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and receives the fault condition input by the user and transmits it to the analysis unit. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the fault condition using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides a specific response method based on the analysis result. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214, and monitors the daily system status. The collection unit is realized, for example, by the control unit 46A of the smart glasses 214, and collects system logs in real time. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the effect of the proposed response method. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and forms a feedback loop based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0153] 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.

[0154] 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 the voice according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0156] 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 performed in a secure state.

[0157] 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.

[0158] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] 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 voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, monitoring unit, collection unit, evaluation unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and receives the fault condition input by the user and transmits it to the analysis unit. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the fault condition using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides a specific response method based on the analysis result. The monitoring unit is realized, for example, by the control unit 46A of the headset type terminal 314, and monitors the daily system status. The collection unit is realized, for example, by the control unit 46A of the headset type terminal 314, and collects system logs in real time. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the effectiveness of the proposed response method. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and forms a feedback loop based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0169] 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.

[0170] 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 the voice according to instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0172] 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 performed in a secure state.

[0173] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0174] 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.

[0175] 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.

[0176] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0177] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0178] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0179] 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 a voice indicating a user input for 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.

[0180] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0181] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0182] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, monitoring unit, collection unit, evaluation unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and receives the fault condition input by the user and transmits it to the analysis unit. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the fault condition using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides a specific response method based on the analysis result. The monitoring unit is realized, for example, by the control unit 46A of the robot 414, and monitors the daily system state. The collection unit is realized, for example, by the control unit 46A of the robot 414, and collects system logs in real time. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the effect of the proposed response method. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and forms a feedback loop based on the evaluation result. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0183] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0184] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0185] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0186] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0187] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0188] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0190] 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.

[0191] 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 Universal Serial Bus (USB) 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.

[0192] In addition, 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 upon request from the data processing device 12.

[0193] 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.

[0194] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0195] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0196] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0197] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0198] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0199] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0200] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0201] (Appendix 1) A reception unit for inputting a fault condition; an analysis unit that analyzes the fault condition input by the reception unit; a providing unit that provides a specific response method based on the result of the analysis by the analyzing unit; A monitoring unit that monitors the system status on a daily basis. A system characterized by: (Appendix 2) Equipped with a collection unit that collects real-time system logs 2. The system of claim 1, (Appendix 3) Equipped with an evaluation section to evaluate the effectiveness of the proposed measures 2. The system of claim 1, (Appendix 4) Equipped with a feedback section that forms a feedback loop 2. The system of claim 1, (Appendix 5) The collecting unit includes: Collect system performance data in cooperation with the monitoring unit. 3. The system of claim 2, (Appendix 6) The evaluation unit is Collaborate with the provision department to evaluate the effectiveness of the proposed response measures 4. The system of claim 3. (Appendix 7) The reception unit is We explain a specific method for estimating a user's emotion and adjusting the input interface for a disability situation based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyzes input history of past failure situations and suggests appropriate input methods 2. The system of claim 1, (Appendix 9) The reception unit is When entering a problem description, the input is automatically completed based on the user's current working context. 2. The system of claim 1, (Appendix 10) The reception unit is We explain a specific method for estimating a user's emotion and prioritizing input fault conditions based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The reception unit is When entering a problem, the system prioritizes input of relevant information based on the user's geographic location. 2. The system of claim 1, (Appendix 12) The reception unit is When a user enters a problem, the system analyzes the user's social media activity and inputs relevant information. 2. The system of claim 1, (Appendix 13) The analysis unit is Explain a specific method to estimate user emotions and adjust the presentation of analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of analysis detail based on the severity of the problem 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of failure. 2. The system of claim 1, (Appendix 16) The analysis unit is Explain a specific method for estimating user sentiment and prioritizing analysis results based on the estimated user sentiment. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the failure occurred 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on fault relevance 2. The system of claim 1, (Appendix 19) The providing unit is Explain a specific method for estimating a user's emotions and adjusting the way responses are expressed based on the estimated user emotions. 2. The system of claim 1, (Appendix 20) The providing unit is When providing, adjust the level of detail of the delivery based on the importance of the response 2. The system of claim 1, (Appendix 21) The providing unit is When serving, different serving algorithms are applied depending on the category of response method. 2. The system of claim 1, (Appendix 22) The providing unit is Explain a specific method for estimating user emotions and prioritizing response methods to be provided based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The providing unit is At the time of delivery, prioritize delivery based on the timing of response methods 2. The system of claim 1, (Appendix 24) The providing unit is When serving, adjust the order of serving based on the relevance of the response methods 2. The system of claim 1, (Appendix 25) The monitoring unit is Explain a specific method to estimate user emotions and adjust the display of monitoring results based on the estimated user emotions. 2. The system of claim 1, (Appendix 26) The monitoring unit is When monitoring, adjust monitoring granularity based on system criticality 2. The system of claim 1, (Appendix 27) The monitoring unit is During monitoring, different monitoring algorithms are applied depending on the category of the system 2. The system of claim 1, (Appendix 28) The monitoring unit is We explain a specific method for estimating user emotions and prioritizing monitoring results based on the estimated user emotions. 2. The system of claim 1, (Appendix 29) The monitoring unit is When monitoring, prioritize monitoring based on when the system occurs 2. The system of claim 1, (Appendix 30) The monitoring unit is When monitoring, adjust the order of monitoring based on system relevance 2. The system of claim 1, (Appendix 31) The collecting unit includes: Explain a specific method to estimate user emotions and prioritize logs based on the estimated user emotions. 3. The system of claim 2, (Appendix 32) The collecting unit includes: At collection time, adjust collection verbosity based on system criticality 3. The system of claim 2, (Appendix 33) The collecting unit includes: Explain how to estimate user emotions and adjust the display method of collected logs based on the estimated user emotions. 3. The system of claim 2, (Appendix 34) The collecting unit includes: At collection time, prioritize collection based on system aging 3. The system of claim 2, (Appendix 35) The evaluation unit is Explain a specific method for estimating user emotions and adjusting the way evaluation results are displayed based on the estimated user emotions. 4. The system of claim 3. (Appendix 36) The evaluation unit is During evaluation, adjust the level of detail in your evaluation based on the importance of the response 4. The system of claim 3. (Appendix 37) The evaluation unit is We explain a specific method for estimating user emotions and prioritizing evaluation results based on the estimated user emotions. 4. The system of claim 3. (Appendix 38) The evaluation unit is During evaluation, prioritize evaluation based on response time 4. The system of claim 3. (Appendix 39) The feedback unit is Explain how to estimate a user's emotions and tailor the feedback based on the estimated emotions. 5. The system of claim 4. (Appendix 40) The feedback unit is When providing feedback, adjust the level of detail in your feedback based on the importance of the system 5. The system of claim 4. (Appendix 41) The feedback unit is Explain how to estimate user sentiment and prioritize feedback based on the estimated sentiment 5. The system of claim 4. (Appendix 42) The feedback unit is During feedback, the system prioritizes feedback based on when it occurs 5. The system of claim 4. [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit for inputting a fault condition; an analysis unit that analyzes the fault condition input by the reception unit; a providing unit that provides a specific response method based on the result of the analysis by the analyzing unit; A monitoring unit that monitors the system status on a daily basis. A system characterized by:

2. Equipped with a collection unit that collects real-time system logs 2. The system of claim 1 .

3. Equipped with an evaluation section to evaluate the effectiveness of the proposed measures 2. The system of claim 1 .

4. Equipped with a feedback section that forms a feedback loop 2. The system of claim 1 .

5. The collecting unit includes: Collect system performance data in cooperation with the monitoring unit.

3. The system of claim 2.

6. The evaluation unit is Collaborate with the provision department to evaluate the effectiveness of the proposed response measures 4. The system of claim 3.

7. The reception unit is We explain a specific method for estimating a user's emotion and adjusting the input interface for a disability situation based on the estimated user's emotion.

2. The system of claim 1 .

8. The reception unit is Analyzes input history of past failure situations and suggests appropriate input methods 2. The system of claim 1 .

Images