System

Abstract

An object of the system according to the embodiment is to efficiently monitor and troubleshoot the IT infrastructure by utilizing the AI.SOLUTION: A system according to an embodiment includes a monitoring unit, a detection unit, a countermeasure unit, and an optimization unit. The monitoring unit monitors the system. The detection unit detects an abnormality based on the data collected by the monitoring unit. The countermeasure unit performs troubleshooting on the abnormality detected by the detection unit. The optimization unit optimizes the system based on the countermeasure executed by the countermeasure unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, management of complex IT infrastructure was carried out manually, making efficient management difficult.

[0005] The system according to the embodiment aims to efficiently monitor and troubleshoot IT infrastructure by utilizing AI. [Means for solving the problem]

[0006] A system according to an embodiment includes a monitoring unit, a detection unit, a countermeasure unit, and an optimization unit. The monitoring unit monitors the system. The detection unit detects anomalies based on data collected by the monitoring unit. The countermeasure unit troubleshoots the anomalies detected by the detection unit. The optimization unit optimizes the system based on the countermeasures implemented by the countermeasure unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently monitor and troubleshoot IT infrastructure by utilizing AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An IT asset management system according to an embodiment of the present invention uses AI to monitor, detect anomalies, troubleshoot, and optimize systems. The IT asset management system monitors systems, detects anomalies based on collected data, troubleshoots the detected anomalies, and optimizes the system based on the implemented countermeasures. For example, the IT asset management system analyzes various sensor and log data in real time to detect anomalies. For example, AI can instantly detect anomalies such as abnormally high server CPU usage or sudden increases in network traffic. The IT asset management system then automatically implements appropriate countermeasures in response to the detected anomalies. For example, AI can automatically respond by restarting a server or changing network settings. Furthermore, the IT asset management system analyzes system performance data and proposes optimal configurations. For example, optimizing resource allocation can improve system efficiency. In this way, an AI-based IT asset management system can address today's complex IT infrastructure. This allows the IT asset management system to automatically monitor, detect anomalies, troubleshoot, and optimize systems. For example, system downtime can be minimized, improving system operational efficiency. Furthermore, IT engineers can focus on developing AI bots and AI orchestration.

[0029] An IT asset management system according to an embodiment includes a monitoring unit, a detection unit, a countermeasure unit, and an optimization unit. The monitoring unit monitors the system. For example, the monitoring unit collects various sensors and log data to monitor the system status. For example, the monitoring unit can monitor the physical status of the system using temperature sensors and pressure sensors. The monitoring unit can also collect access logs and error logs to monitor the operating status of the system. The monitoring unit can collect data in real time to provide basic data for detecting anomalies. The detection unit detects anomalies based on the collected data. The detection unit identifies anomalies using, for example, a machine learning algorithm. For example, the detection unit can detect system anomalies using an anomaly detection algorithm. The detection unit can also identify the type of anomaly using a classification algorithm. The detection unit can analyze the collected data and identify the cause of the anomaly. The countermeasure unit troubleshoots the detected anomaly. For example, the countermeasure unit selects an appropriate countermeasure using, for example, a rule-based system. For example, the countermeasure unit can select an optimal countermeasure for the anomaly using a rule engine. The countermeasure unit can also automatically execute a countermeasure for the anomaly using a rule set. Furthermore, the countermeasure unit can select an appropriate countermeasure depending on the type of anomaly. The optimization unit optimizes the system based on the executed countermeasure. The optimization unit, for example, calculates resource allocation using a data analysis algorithm. For example, the optimization unit can optimize system resource allocation using regression analysis. The optimization unit can also efficiently allocate system resources using clustering. Furthermore, the optimization unit can analyze system performance data and propose optimal settings. This allows the IT asset management system according to the embodiment to automatically monitor, detect anomalies, troubleshoot, and optimize the system. For example, system downtime can be minimized, improving system operational efficiency. Furthermore, IT engineers can focus on developing AI Bots and AI Orchestration.

[0030] The monitoring unit can collect various sensor or log data. The monitoring unit can monitor the physical state of the system using, for example, a temperature sensor or a pressure sensor. For example, the monitoring unit can monitor the temperature of a server using a temperature sensor and detect abnormal temperature increases. The monitoring unit can also monitor the pressure of a cooling system using a pressure sensor and detect abnormal pressure fluctuations. The monitoring unit can also collect access logs and error logs to monitor the operating state of the system. For example, the monitoring unit can analyze access logs to detect signs of unauthorized access. The monitoring unit can also analyze error logs to detect abnormal system operation. In this way, the monitoring unit can collect various sensor and log data to obtain a detailed understanding of the system state. Some or all of the above-described 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 collected sensor data and log data into a generation AI and have the generation AI detect anomalies.

[0031] The detection unit can identify anomalies using a machine learning algorithm. The detection unit can detect system anomalies using, for example, an anomaly detection algorithm. For example, the detection unit can use the anomaly detection algorithm to detect anomalies such as abnormally high CPU usage on a server or a sudden increase in network traffic. The detection unit can also identify the type of anomaly using a classification algorithm. For example, the detection unit can use the classification algorithm to determine whether the anomaly is caused by a hardware failure or a software bug. The detection unit can also analyze collected data and identify the cause of the anomaly. For example, the detection unit can use a data analysis algorithm to identify the cause of the anomaly and propose appropriate countermeasures. Thus, the use of a machine learning algorithm improves the accuracy of anomaly detection. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input collected data to a generation AI and cause the generation AI to identify the anomaly.

[0032] The countermeasure unit can select an appropriate countermeasure using a rule-based system. The countermeasure unit can select the optimal countermeasure for the anomaly using, for example, a rule engine. For example, the countermeasure unit can use the rule engine to select a countermeasure such as restarting the server or changing the network settings. The countermeasure unit can also automatically execute a countermeasure for the anomaly using a rule set. For example, the countermeasure unit can use the rule set to automatically restart a server when an anomaly is detected. Furthermore, the countermeasure unit can select an appropriate countermeasure depending on the type of anomaly. For example, the countermeasure unit can suggest replacing the hardware if the anomaly is caused by a hardware failure, and suggest updating the software if the anomaly is caused by a software bug. This enables quick and appropriate countermeasures to be taken using a rule-based system. Some or all of the above-described processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input the type of anomaly into a generation AI and have the generation AI select the optimal countermeasure.

[0033] The optimization unit can calculate resource allocation using a data analysis algorithm. The optimization unit can optimize system resource allocation using, for example, regression analysis. For example, the optimization unit can optimize a server's CPU usage rate or memory usage using regression analysis. The optimization unit can also efficiently allocate system resources using clustering. For example, the optimization unit can optimize network traffic using clustering. Furthermore, the optimization unit can analyze system performance data and propose optimal settings. For example, the optimization unit can propose settings for improving system performance using a data analysis algorithm. This makes it possible to optimize resource allocation using a data analysis algorithm. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input collected performance data to a generation AI and cause the generation AI to perform resource allocation calculations.

[0034] The monitoring unit can determine the focus of monitoring based on the system's past performance data. For example, the monitoring unit can focus monitoring on components that have frequently experienced problems in the past. For example, the monitoring unit can analyze past performance data and focus monitoring on specific components. The monitoring unit can also focus monitoring on areas that are prone to problems during specific time periods based on past performance data. For example, the monitoring unit can analyze the system load status during specific time periods based on past data and focus monitoring. Furthermore, the monitoring unit can adjust the focus of monitoring for specific events or situations based on past data. For example, the monitoring unit can analyze past event data and focus monitoring when a specific event occurs. This enables effective monitoring by referring to past performance data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input past performance data into a generation AI and have the generation AI determine the focus of monitoring.

[0035] The monitoring unit can apply individual monitoring algorithms to specific components of the system. For example, the monitoring unit can apply a real-time monitoring algorithm to a server's CPU usage rate. For example, the monitoring unit can monitor the server's CPU usage rate in real time and detect anomalies. The monitoring unit can also apply an anomaly detection algorithm to network traffic. For example, the monitoring unit can monitor network traffic and detect an abnormal increase in traffic. Furthermore, the monitoring unit can apply a predictive algorithm based on historical data to database performance. For example, the monitoring unit can analyze database performance data and predict future performance. This improves monitoring accuracy by applying an appropriate monitoring algorithm to a specific component. Some or all of the above-described 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 data of a specific component to a generation AI and have the generation AI apply a monitoring algorithm.

[0036] The monitoring unit can adjust the level of monitoring detail according to the system load status. For example, when the system load is high, the monitoring unit performs detailed monitoring to aim for early detection of abnormalities. For example, when the system load is high, the monitoring unit can collect detailed data and detect abnormalities early. The monitoring unit can also reduce the level of monitoring detail when the system load is low to save resources. For example, when the system load is low, the monitoring unit can reduce the monitoring frequency to save resources. Furthermore, the monitoring unit can adjust the level of monitoring detail in real time according to load fluctuations. For example, the monitoring unit can monitor the system load status in real time and adjust the level of monitoring detail according to load fluctuations. As a result, adjusting the level of monitoring detail according to the system load status enables efficient use of resources. Some 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 system load data to a generation AI and cause the generation AI to adjust the level of monitoring detail.

[0037] The monitoring unit can determine the monitoring range based on the geographical distribution of the system. For example, for a geographically distributed system, the monitoring unit can set a different monitoring range for each region. For example, for a geographically distributed data center, the monitoring unit can set a different monitoring range for each region. The monitoring unit can also perform focused monitoring of geographically important locations. For example, the monitoring unit can perform focused monitoring of geographically important locations. Furthermore, the monitoring unit can determine monitoring priorities based on the geographical distribution. For example, the monitoring unit can determine monitoring priorities based on the geographical distribution and prioritize monitoring of important regions. This enables effective monitoring by taking the geographical distribution into consideration. Some or all of the above-described 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 geographical distribution data of the system to a generation AI and have the generation AI determine the monitoring range.

[0038] The monitoring unit can improve the accuracy of monitoring based on related literature of the system. The monitoring unit, for example, refers to the latest research papers to improve the monitoring algorithm. For example, the monitoring unit can refer to the latest research papers to improve the monitoring algorithm. The monitoring unit can also improve the accuracy of monitoring based on related technical literature. For example, the monitoring unit can improve the monitoring algorithm based on related technical literature. Furthermore, the monitoring unit can improve the accuracy of monitoring by referring to past cases. For example, the monitoring unit can improve the monitoring algorithm by referring to past cases. As a result, the accuracy of monitoring is improved by referring to related literature. Some 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 related literature data into the generation AI and cause the generation AI to improve the monitoring algorithm.

[0039] The monitoring unit can determine monitoring priorities based on the market value of the system. The monitoring unit, for example, prioritizes monitoring of high-value systems. For example, the monitoring unit can evaluate the market value of the system and prioritize monitoring of high-value systems. The monitoring unit can also prioritize monitoring of components with high market value. For example, the monitoring unit can prioritize monitoring of components with high market value. Furthermore, the monitoring unit can adjust the monitoring priorities in real time in accordance with fluctuations in market value. For example, the monitoring unit can adjust the monitoring priorities in real time in accordance with fluctuations in market value and prioritize monitoring of important systems. This allows important systems to be prioritized by taking market value into consideration. Some or all of the above-described 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 market value data of the system to a generation AI and have the generation AI determine the monitoring priorities.

[0040] The detection unit can improve the accuracy of anomaly detection based on the interrelationships between systems. The detection unit can improve the accuracy of anomaly detection by taking into account, for example, the interrelationships between a server and a network. For example, the detection unit can analyze the interrelationships between a server and a network to improve the accuracy of anomaly detection. The detection unit can also improve the accuracy of anomaly detection by taking into account the interrelationships between a database and an application. For example, the detection unit can analyze the interrelationships between a database and an application to improve the accuracy of anomaly detection. Furthermore, the detection unit can improve the accuracy of anomaly detection by taking into account the interdependencies of each component. For example, the detection unit can analyze the interdependencies of each component to improve the accuracy of anomaly detection. As a result, the accuracy of anomaly detection is improved by taking into account the interrelationships between systems. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input system interrelationship data to a generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0041] The detection unit can apply individual detection algorithms to specific components of the system. For example, the detection unit can apply a real-time anomaly detection algorithm to a server's CPU usage. For example, the detection unit can monitor the server's CPU usage in real time and detect anomalies. The detection unit can also apply an anomaly detection algorithm to network traffic. For example, the detection unit can monitor network traffic and detect an abnormal increase in traffic. Furthermore, the detection unit can apply a predictive algorithm based on historical data to database performance. For example, the detection unit can analyze database performance data and predict future performance. This improves the accuracy of anomaly detection by applying an appropriate detection algorithm to a specific component. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input data of a specific component to a generation AI and cause the generation AI to apply a detection algorithm.

[0042] The detection unit can adjust the accuracy of anomaly detection according to the system load status. For example, when the system load is high, the detection unit performs detailed anomaly detection to aim for early detection of anomalies. For example, when the system load is high, the detection unit can collect detailed data and detect anomalies early. The detection unit can also reduce the level of detail of anomaly detection to save resources when the system load is low. For example, when the system load is low, the detection unit can reduce the frequency of anomaly detection to save resources. Furthermore, the detection unit can adjust the level of detail of anomaly detection in real time according to load fluctuations. For example, the detection unit can monitor the system load status in real time and adjust the level of detail of anomaly detection according to load fluctuations. This enables efficient use of resources by adjusting the level of detail of anomaly detection according to the system load status. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input system load data to a generation AI and cause the generation AI to adjust the level of detail of anomaly detection.

[0043] The detection unit can determine the anomaly detection range based on the geographical distribution of the system. For example, for a geographically distributed system, the detection unit can set a different anomaly detection range for each region. For example, for geographically distributed data centers, the detection unit can set a different anomaly detection range for each region. The detection unit can also perform anomaly detection with a focus on geographically important locations. For example, the detection unit can perform anomaly detection with a focus on geographically important locations. Furthermore, the detection unit can determine the priority of anomaly detection based on the geographical distribution. For example, the detection unit can determine the priority of anomaly detection based on the geographical distribution and prioritize anomaly detection in important regions. This enables effective anomaly detection by taking the geographical distribution into consideration. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the geographical distribution data of the system to the generation AI and cause the generation AI to determine the anomaly detection range.

[0044] The detection unit can improve the accuracy of anomaly detection based on related literature of the system. The detection unit, for example, refers to the latest research papers to improve the anomaly detection algorithm. For example, the detection unit can refer to the latest research papers to improve the anomaly detection algorithm. The detection unit can also improve the accuracy of anomaly detection based on related technical literature. For example, the detection unit can improve the anomaly detection algorithm based on related technical literature. Furthermore, the detection unit can improve the accuracy of anomaly detection by referring to past cases. For example, the detection unit can improve the anomaly detection algorithm by referring to past cases. As a result, the accuracy of anomaly detection is improved by referring to related literature. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input related literature data to the generation AI and cause the generation AI to improve the anomaly detection algorithm.

[0045] The detection unit can determine the priority of anomaly detection based on the market value of the system. The detection unit, for example, prioritizes anomaly detection for high-value systems. For example, the detection unit can evaluate the market value of the system and prioritize anomaly detection for high-value systems. The detection unit can also focus anomaly detection on components with high market value. For example, the detection unit can focus anomaly detection on components with high market value. Furthermore, the detection unit can adjust the priority of anomaly detection in real time in accordance with fluctuations in market value. For example, the detection unit can adjust the priority of anomaly detection in real time in accordance with fluctuations in market value and prioritize anomaly detection for important systems. This allows anomaly detection to be prioritized for important systems by taking market value into consideration. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input market value data of the system to the generation AI and cause the generation AI to determine the priority of anomaly detection.

[0046] The countermeasure unit can select appropriate countermeasures based on the system's past countermeasure data. For example, the countermeasure unit can prioritize selecting countermeasures that were effective in the past. For example, the countermeasure unit can analyze past countermeasure data and prioritize selecting countermeasures that were effective. The countermeasure unit can also propose optimal countermeasures based on past countermeasure data. For example, the countermeasure unit can analyze past countermeasure data and propose optimal countermeasures. Furthermore, the countermeasure unit can analyze past countermeasure history and select the most effective countermeasure. For example, the countermeasure unit can analyze past countermeasure history and select the most effective countermeasure. This makes it possible to implement effective countermeasures by referring to past countermeasure data. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input past countermeasure data into a generation AI and have the generation AI select appropriate countermeasures.

[0047] The countermeasure unit can apply individual countermeasure algorithms to specific components of the system. For example, the countermeasure unit applies a restart algorithm to a server abnormality. For example, the countermeasure unit can apply a restart algorithm when a server abnormality is detected. The countermeasure unit can also apply a configuration change algorithm to a network abnormality. For example, the countermeasure unit can apply a configuration change algorithm when a network abnormality is detected. Furthermore, the countermeasure unit can apply a data repair algorithm to a database abnormality. For example, the countermeasure unit can apply a data repair algorithm when a database abnormality is detected. This improves the accuracy of countermeasures by applying an appropriate countermeasure algorithm to a specific component. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input data of a specific component to a generation AI and have the generation AI apply a countermeasure algorithm.

[0048] The countermeasure unit can adjust the accuracy of the countermeasures according to the system load status. For example, when the system load is high, the countermeasure unit can prioritize quick and simple countermeasures. For example, when the system load is high, the countermeasure unit can prioritize quick and simple countermeasures. Furthermore, the countermeasure unit can also implement detailed countermeasures when the system load is low. For example, when the system load is low, the countermeasure unit can implement detailed countermeasures. Furthermore, the countermeasure unit can adjust the level of detail of the countermeasures in real time according to load fluctuations. For example, the countermeasure unit can monitor the system load status in real time and adjust the level of detail of the countermeasures according to load fluctuations. This enables efficient use of resources by adjusting the level of detail of the countermeasures according to the system load status. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input system load data to a generation AI and have the generation AI adjust the level of detail of the countermeasures.

[0049] The countermeasures department can determine the scope of countermeasures based on the geographical distribution of the system. For example, for a geographically distributed system, the countermeasures department can set a different countermeasure scope for each region. For example, for a geographically distributed data center, the countermeasures department can set a different countermeasure scope for each region. The countermeasures department can also implement countermeasures intensively for geographically important bases. For example, the countermeasures department can implement countermeasures intensively for geographically important bases. Furthermore, the countermeasures department can determine the priority of countermeasures based on the geographical distribution. For example, the countermeasures department can determine the priority of countermeasures based on the geographical distribution and prioritize countermeasures in important regions. This enables effective countermeasures by taking the geographical distribution into consideration. Some or all of the above-described processing in the countermeasures department may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasures department can input the geographical distribution data of the system into a generation AI and have the generation AI determine the scope of countermeasures.

[0050] The countermeasure unit can improve the accuracy of the countermeasures based on related literature of the system. The countermeasure unit, for example, refers to the latest research papers to improve the countermeasure algorithm. For example, the countermeasure unit can refer to the latest research papers to improve the countermeasure algorithm. The countermeasure unit can also improve the accuracy of the countermeasures based on related technical literature. For example, the countermeasure unit can improve the countermeasure algorithm based on related technical literature. Furthermore, the countermeasure unit can improve the accuracy of the countermeasures by referring to past cases. For example, the countermeasure unit can improve the countermeasure algorithm by referring to past cases. As a result, the accuracy of the countermeasures is improved by referring to related literature. Some or all of the above-mentioned processing in the countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the countermeasure unit can input related literature data into the generation AI and have the generation AI improve the countermeasure algorithm.

[0051] The countermeasures department can determine the priority of countermeasures based on the market value of the system. The countermeasures department, for example, prioritizes countermeasures for high-value systems. For example, the countermeasures department can evaluate the market value of the system and prioritize countermeasures for high-value systems. The countermeasures department can also prioritize countermeasures for components with high market value. For example, the countermeasures department can prioritize countermeasures for components with high market value. Furthermore, the countermeasures department can adjust the priority of countermeasures in real time in accordance with fluctuations in market value. For example, the countermeasures department can adjust the priority of countermeasures in real time in accordance with fluctuations in market value and prioritize countermeasures for important systems. This allows countermeasures to be prioritized for important systems by taking market value into consideration. Some or all of the above-mentioned processing in the countermeasures department may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasures department can input market value data of the system into a generation AI and have the generation AI determine the priority of countermeasures.

[0052] The optimization unit can propose appropriate settings based on past performance data of the system. For example, the optimization unit prioritizes the proposal of settings that were effective in the past. For example, the optimization unit can analyze past performance data and prioritize the proposal of settings that were effective. The optimization unit can also propose optimal settings based on past performance data. For example, the optimization unit can analyze past performance data and propose optimal settings. Furthermore, the optimization unit can analyze past performance history and propose the most effective settings. For example, the optimization unit can analyze past performance history and propose the most effective settings. This enables effective optimization by referring to past performance data. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input past performance data to a generation AI and cause the generation AI to propose appropriate settings.

[0053] The optimization unit can apply individual optimization algorithms to specific components of the system. For example, the optimization unit can apply a real-time optimization algorithm to server resource allocation. For example, the optimization unit can apply an algorithm that optimizes server resource allocation in real time. The optimization unit can also apply an optimization algorithm to network bandwidth. For example, the optimization unit can apply an algorithm that optimizes network bandwidth. Furthermore, the optimization unit can apply an optimization algorithm based on historical data to database query performance. For example, the optimization unit can apply an algorithm that optimizes database query performance. This improves optimization accuracy by applying an appropriate optimization algorithm to a specific component. Some or all of the above-mentioned processing in the optimization unit may be performed using, or without, AI. For example, the optimization unit can input data of a specific component to a generation AI and cause the generation AI to apply an optimization algorithm.

[0054] The optimization unit can adjust the accuracy of optimization according to the system load status. For example, when the system load is high, the optimization unit can prioritize quick and simple optimization. For example, when the system load is high, the optimization unit can prioritize quick and simple optimization. The optimization unit can also perform detailed optimization when the system load is low. For example, when the system load is low, the optimization unit can perform detailed optimization. Furthermore, the optimization unit can adjust the level of detail of optimization in real time according to load fluctuations. For example, the optimization unit can monitor the system load status in real time and adjust the level of detail of optimization according to load fluctuations. This enables efficient use of resources by adjusting the level of detail of optimization according to the system load status. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input system load data to the generation AI and cause the generation AI to adjust the level of detail of optimization.

[0055] The optimization unit can determine the optimization range based on the geographical distribution of the system. For example, for a geographically distributed system, the optimization unit can set a different optimization range for each region. For example, for geographically distributed data centers, the optimization unit can set a different optimization range for each region. The optimization unit can also perform focused optimization for geographically important locations. For example, the optimization unit can perform focused optimization for geographically important locations. Furthermore, the optimization unit can determine optimization priorities based on the geographical distribution. For example, the optimization unit can determine optimization priorities based on the geographical distribution and prioritize optimization for important regions. This enables effective optimization by taking geographical distribution into consideration. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input geographical distribution data of the system to the generation AI and cause the generation AI to determine the optimization range.

[0056] The optimization unit can improve the accuracy of optimization based on related literature of the system. The optimization unit, for example, refers to the latest research papers to improve the optimization algorithm. For example, the optimization unit can refer to the latest research papers to improve the optimization algorithm. The optimization unit can also improve the accuracy of optimization based on related technical literature. For example, the optimization unit can improve the optimization algorithm based on related technical literature. Furthermore, the optimization unit can improve the accuracy of optimization by referring to past cases. For example, the optimization unit can improve the optimization algorithm by referring to past cases. As a result, the accuracy of optimization is improved by referring to related literature. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input related literature data into the generation AI and cause the generation AI to improve the optimization algorithm.

[0057] The optimization unit can determine optimization priorities based on the market value of the system. The optimization unit, for example, prioritizes optimization of high-value systems. For example, the optimization unit can evaluate the market value of the system and prioritize optimization of high-value systems. The optimization unit can also prioritize optimization of components with high market value. For example, the optimization unit can prioritize optimization of components with high market value. Furthermore, the optimization unit can adjust the optimization priorities in real time in accordance with fluctuations in market value. For example, the optimization unit can adjust the optimization priorities in real time in accordance with fluctuations in market value and prioritize optimization of important systems. This allows important systems to be prioritized for optimization by taking market value into consideration. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input market value data of the system to the generation AI and cause the generation AI to determine the optimization priorities.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The monitoring unit can monitor the energy consumption of the system and optimize energy efficiency. For example, the monitoring unit can collect energy consumption data for each component and identify areas with low energy efficiency. The monitoring unit can also identify peak energy consumption times and propose measures to level out energy consumption. Furthermore, the monitoring unit can analyze the energy consumption data and propose optimal settings to improve energy efficiency. This can improve the energy efficiency of the system.

[0060] The detection unit can detect system security threats and issue alerts in real time. For example, the detection unit can detect abnormal login attempts and signs of unauthorized access. The detection unit can also monitor malware activity and detect signs of infection early. Furthermore, the detection unit can analyze network traffic and detect signs of DDoS attacks. This strengthens system security and enables rapid response.

[0061] The countermeasures department can perform preventive maintenance to improve system performance. For example, the countermeasures department can automatically schedule regular hardware inspections and software updates. The countermeasures department can also analyze system performance data, identify potential problems in advance, and implement preventative measures. Furthermore, the countermeasures department can suggest optimal maintenance schedules based on system usage. This helps minimize system downtime and improve performance.

[0062] The optimization unit can optimize performance by dynamically allocating system resources. For example, the optimization unit can monitor resource usage in real time and reallocate resources as needed. The optimization unit can also allocate resources preferentially to specific tasks or applications. Furthermore, the optimization unit can dynamically adjust resource allocation according to the system load status. This maximizes system performance and enables efficient resource utilization.

[0063] The monitoring unit can collect environmental data about the system and propose measures to minimize its impact on the environment. For example, the monitoring unit can collect temperature, humidity, and energy consumption data about the system and evaluate its impact on the environment. The monitoring unit can also analyze the environmental data and propose measures to improve energy efficiency. Furthermore, the monitoring unit can propose settings to optimize system operation based on the environmental data. This minimizes the system's impact on the environment and enables sustainable operation.

[0064] The detection unit can analyze system performance data and predict future anomalies. For example, the detection unit can predict the possibility of future anomalies based on past performance data. The detection unit can also propose preventive measures based on the results of the anomaly prediction. Furthermore, the detection unit can propose settings to optimize system operation based on the results of the anomaly prediction. This makes it possible to predict future anomalies and take measures in advance, thereby improving system stability.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The monitoring unit monitors the system. The monitoring unit collects various sensor and log data to monitor the system status. For example, it uses temperature and pressure sensors to monitor the physical status of the system, and collects access and error logs to monitor the system's operating status. It also collects data in real time to provide basic data for detecting abnormalities. Step 2: The detection unit detects anomalies based on the collected data. The detection unit uses machine learning algorithms and anomaly detection algorithms to identify anomalies, and a classification algorithm to identify the type of anomaly. The detection unit also analyzes the collected data and identifies the cause of the anomaly. Step 3: The countermeasures department troubleshoots the detected anomalies. The countermeasures department uses a rule-based system or rule engine to select appropriate countermeasures and automatically executes countermeasures against the anomaly using a rule set. The appropriate countermeasure can also be selected depending on the type of anomaly. Step 4: The optimization unit optimizes the system based on the implemented measures. The optimization unit uses data analysis algorithms to calculate resource allocation and uses regression analysis and clustering to optimize the system's resource allocation. It also analyzes system performance data and proposes optimal settings.

[0067] (Example 2) An IT asset management system according to an embodiment of the present invention uses AI to monitor, detect anomalies, troubleshoot, and optimize systems. The IT asset management system monitors systems, detects anomalies based on collected data, troubleshoots the detected anomalies, and optimizes the system based on the implemented countermeasures. For example, the IT asset management system analyzes various sensor and log data in real time to detect anomalies. For example, AI can instantly detect anomalies such as abnormally high server CPU usage or sudden increases in network traffic. The IT asset management system then automatically implements appropriate countermeasures in response to the detected anomalies. For example, AI can automatically respond by restarting a server or changing network settings. Furthermore, the IT asset management system analyzes system performance data and proposes optimal configurations. For example, optimizing resource allocation can improve system efficiency. In this way, an AI-based IT asset management system can address today's complex IT infrastructure. This allows the IT asset management system to automatically monitor, detect anomalies, troubleshoot, and optimize systems. For example, system downtime can be minimized, improving system operational efficiency. Furthermore, IT engineers can focus on developing AI bots and AI orchestration.

[0068] An IT asset management system according to an embodiment includes a monitoring unit, a detection unit, a countermeasure unit, and an optimization unit. The monitoring unit monitors the system. For example, the monitoring unit collects various sensors and log data to monitor the system status. For example, the monitoring unit can monitor the physical status of the system using temperature sensors and pressure sensors. The monitoring unit can also collect access logs and error logs to monitor the operating status of the system. The monitoring unit can collect data in real time to provide basic data for detecting anomalies. The detection unit detects anomalies based on the collected data. The detection unit identifies anomalies using, for example, a machine learning algorithm. For example, the detection unit can detect system anomalies using an anomaly detection algorithm. The detection unit can also identify the type of anomaly using a classification algorithm. The detection unit can analyze the collected data and identify the cause of the anomaly. The countermeasure unit troubleshoots the detected anomaly. For example, the countermeasure unit selects an appropriate countermeasure using, for example, a rule-based system. For example, the countermeasure unit can select an optimal countermeasure for the anomaly using a rule engine. The countermeasure unit can also automatically execute a countermeasure for the anomaly using a rule set. Furthermore, the countermeasure unit can select an appropriate countermeasure depending on the type of anomaly. The optimization unit optimizes the system based on the executed countermeasure. The optimization unit, for example, calculates resource allocation using a data analysis algorithm. For example, the optimization unit can optimize system resource allocation using regression analysis. The optimization unit can also efficiently allocate system resources using clustering. Furthermore, the optimization unit can analyze system performance data and propose optimal settings. This allows the IT asset management system according to the embodiment to automatically monitor, detect anomalies, troubleshoot, and optimize the system. For example, system downtime can be minimized, improving system operational efficiency. Furthermore, IT engineers can focus on developing AI Bots and AI Orchestration.

[0069] The monitoring unit can collect various sensor or log data. The monitoring unit can monitor the physical state of the system using, for example, a temperature sensor or a pressure sensor. For example, the monitoring unit can monitor the temperature of a server using a temperature sensor and detect abnormal temperature increases. The monitoring unit can also monitor the pressure of a cooling system using a pressure sensor and detect abnormal pressure fluctuations. The monitoring unit can also collect access logs and error logs to monitor the operating state of the system. For example, the monitoring unit can analyze access logs to detect signs of unauthorized access. The monitoring unit can also analyze error logs to detect abnormal system operation. In this way, the monitoring unit can collect various sensor and log data to obtain a detailed understanding of the system state. Some or all of the above-described 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 collected sensor data and log data into a generation AI and have the generation AI detect anomalies.

[0070] The detection unit can identify anomalies using a machine learning algorithm. The detection unit can detect system anomalies using, for example, an anomaly detection algorithm. For example, the detection unit can use the anomaly detection algorithm to detect anomalies such as abnormally high CPU usage on a server or a sudden increase in network traffic. The detection unit can also identify the type of anomaly using a classification algorithm. For example, the detection unit can use the classification algorithm to determine whether the anomaly is caused by a hardware failure or a software bug. The detection unit can also analyze collected data and identify the cause of the anomaly. For example, the detection unit can use a data analysis algorithm to identify the cause of the anomaly and propose appropriate countermeasures. Thus, the use of a machine learning algorithm improves the accuracy of anomaly detection. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input collected data to a generation AI and cause the generation AI to identify the anomaly.

[0071] The countermeasure unit can select an appropriate countermeasure using a rule-based system. The countermeasure unit can select the optimal countermeasure for the anomaly using, for example, a rule engine. For example, the countermeasure unit can use the rule engine to select a countermeasure such as restarting the server or changing the network settings. The countermeasure unit can also automatically execute a countermeasure for the anomaly using a rule set. For example, the countermeasure unit can use the rule set to automatically restart a server when an anomaly is detected. Furthermore, the countermeasure unit can select an appropriate countermeasure depending on the type of anomaly. For example, the countermeasure unit can suggest replacing the hardware if the anomaly is caused by a hardware failure, and suggest updating the software if the anomaly is caused by a software bug. This enables quick and appropriate countermeasures to be taken using a rule-based system. Some or all of the above-described processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input the type of anomaly into a generation AI and have the generation AI select the optimal countermeasure.

[0072] The optimization unit can calculate resource allocation using a data analysis algorithm. The optimization unit can optimize system resource allocation using, for example, regression analysis. For example, the optimization unit can optimize a server's CPU usage rate or memory usage using regression analysis. The optimization unit can also efficiently allocate system resources using clustering. For example, the optimization unit can optimize network traffic using clustering. Furthermore, the optimization unit can analyze system performance data and propose optimal settings. For example, the optimization unit can propose settings for improving system performance using a data analysis algorithm. This makes it possible to optimize resource allocation using a data analysis algorithm. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input collected performance data to a generation AI and cause the generation AI to perform resource allocation calculations.

[0073] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the user's emotions. For example, if the user is feeling stressed, the monitoring unit can increase the monitoring frequency to ensure system stability. For example, the monitoring unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the monitoring unit can reduce the monitoring frequency to save resources. For example, the monitoring unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the monitoring unit can optimize the monitoring frequency to enable a quick response. For example, the monitoring unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the monitoring unit to adjust the monitoring frequency according to the user's emotions, thereby ensuring system stability. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input user emotion data to the generation AI and cause the generation AI to adjust the monitoring frequency.

[0074] The monitoring unit can determine the focus of monitoring based on the system's past performance data. For example, the monitoring unit can focus monitoring on components that have frequently experienced problems in the past. For example, the monitoring unit can analyze past performance data and focus monitoring on specific components. The monitoring unit can also focus monitoring on areas that are prone to problems during specific time periods based on past performance data. For example, the monitoring unit can analyze the system load status during specific time periods based on past data and focus monitoring. Furthermore, the monitoring unit can adjust the focus of monitoring for specific events or situations based on past data. For example, the monitoring unit can analyze past event data and focus monitoring when a specific event occurs. This enables effective monitoring by referring to past performance data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input past performance data into a generation AI and have the generation AI determine the focus of monitoring.

[0075] The monitoring unit can apply individual monitoring algorithms to specific components of the system. For example, the monitoring unit can apply a real-time monitoring algorithm to a server's CPU usage rate. For example, the monitoring unit can monitor the server's CPU usage rate in real time and detect anomalies. The monitoring unit can also apply an anomaly detection algorithm to network traffic. For example, the monitoring unit can monitor network traffic and detect an abnormal increase in traffic. Furthermore, the monitoring unit can apply a predictive algorithm based on historical data to database performance. For example, the monitoring unit can analyze database performance data and predict future performance. This improves monitoring accuracy by applying an appropriate monitoring algorithm to a specific component. Some or all of the above-described 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 data of a specific component to a generation AI and have the generation AI apply a monitoring algorithm.

[0076] The monitoring unit can adjust the level of monitoring detail according to the system load status. For example, when the system load is high, the monitoring unit performs detailed monitoring to aim for early detection of abnormalities. For example, when the system load is high, the monitoring unit can collect detailed data and detect abnormalities early. The monitoring unit can also reduce the level of monitoring detail when the system load is low to save resources. For example, when the system load is low, the monitoring unit can reduce the monitoring frequency to save resources. Furthermore, the monitoring unit can adjust the level of monitoring detail in real time according to load fluctuations. For example, the monitoring unit can monitor the system load status in real time and adjust the level of monitoring detail according to load fluctuations. As a result, adjusting the level of monitoring detail according to the system load status enables efficient use of resources. Some 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 system load data to a generation AI and cause the generation AI to adjust the level of monitoring detail.

[0077] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the user's emotions. For example, if the user is nervous, the monitoring unit can provide a simple, highly visible display method. For example, the monitoring unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide a simple display method. Furthermore, if the user is relaxed, the monitoring unit can provide a display method that includes detailed information. For example, the monitoring unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a detailed display method. Furthermore, if the user is in a hurry, the monitoring unit can provide a display method that focuses on the key points. For example, the monitoring unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and provide a display method that focuses on the key points. This allows the display method of the monitoring results to be adjusted according to the user's emotions, thereby enabling a display that is easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method of the monitoring results.

[0078] The monitoring unit can determine the monitoring range based on the geographical distribution of the system. For example, for a geographically distributed system, the monitoring unit can set a different monitoring range for each region. For example, for a geographically distributed data center, the monitoring unit can set a different monitoring range for each region. The monitoring unit can also perform focused monitoring of geographically important locations. For example, the monitoring unit can perform focused monitoring of geographically important locations. Furthermore, the monitoring unit can determine monitoring priorities based on the geographical distribution. For example, the monitoring unit can determine monitoring priorities based on the geographical distribution and prioritize monitoring of important regions. This enables effective monitoring by taking the geographical distribution into consideration. Some or all of the above-described 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 geographical distribution data of the system to a generation AI and have the generation AI determine the monitoring range.

[0079] The monitoring unit can improve the accuracy of monitoring based on related literature of the system. The monitoring unit, for example, refers to the latest research papers to improve the monitoring algorithm. For example, the monitoring unit can refer to the latest research papers to improve the monitoring algorithm. The monitoring unit can also improve the accuracy of monitoring based on related technical literature. For example, the monitoring unit can improve the monitoring algorithm based on related technical literature. Furthermore, the monitoring unit can improve the accuracy of monitoring by referring to past cases. For example, the monitoring unit can improve the monitoring algorithm by referring to past cases. As a result, the accuracy of monitoring is improved by referring to related literature. Some 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 related literature data into the generation AI and cause the generation AI to improve the monitoring algorithm.

[0080] The monitoring unit can determine monitoring priorities based on the market value of the system. The monitoring unit, for example, prioritizes monitoring of high-value systems. For example, the monitoring unit can evaluate the market value of the system and prioritize monitoring of high-value systems. The monitoring unit can also prioritize monitoring of components with high market value. For example, the monitoring unit can prioritize monitoring of components with high market value. Furthermore, the monitoring unit can adjust the monitoring priorities in real time in accordance with fluctuations in market value. For example, the monitoring unit can adjust the monitoring priorities in real time in accordance with fluctuations in market value and prioritize monitoring of important systems. This allows important systems to be prioritized by taking market value into consideration. Some or all of the above-described 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 market value data of the system to a generation AI and have the generation AI determine the monitoring priorities.

[0081] The detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the user's emotions. For example, if the user is feeling stressed, the detection unit can set stricter anomaly detection criteria. For example, the detection unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and set stricter anomaly detection criteria. The detection unit can also set lenient anomaly detection criteria if the user is relaxed. For example, the detection unit can record the user's voice, estimate the user's emotions using voice analysis technology, and set lenient anomaly detection criteria. Furthermore, if the user is in a hurry, the detection unit can optimize the anomaly detection criteria to enable a prompt response. For example, the detection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and optimize the anomaly detection criteria. This allows for appropriate anomaly detection by adjusting the anomaly detection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI, or may be performed without using AI. For example, the detection unit may input user emotion data to the generation AI and cause the generation AI to adjust the anomaly detection criteria.

[0082] The detection unit can improve the accuracy of anomaly detection based on the interrelationships between systems. The detection unit can improve the accuracy of anomaly detection by taking into account, for example, the interrelationships between a server and a network. For example, the detection unit can analyze the interrelationships between a server and a network to improve the accuracy of anomaly detection. The detection unit can also improve the accuracy of anomaly detection by taking into account the interrelationships between a database and an application. For example, the detection unit can analyze the interrelationships between a database and an application to improve the accuracy of anomaly detection. Furthermore, the detection unit can improve the accuracy of anomaly detection by taking into account the interdependencies of each component. For example, the detection unit can analyze the interdependencies of each component to improve the accuracy of anomaly detection. As a result, the accuracy of anomaly detection is improved by taking into account the interrelationships between systems. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input system interrelationship data to a generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0083] The detection unit can apply individual detection algorithms to specific components of the system. For example, the detection unit can apply a real-time anomaly detection algorithm to a server's CPU usage. For example, the detection unit can monitor the server's CPU usage in real time and detect anomalies. The detection unit can also apply an anomaly detection algorithm to network traffic. For example, the detection unit can monitor network traffic and detect an abnormal increase in traffic. Furthermore, the detection unit can apply a predictive algorithm based on historical data to database performance. For example, the detection unit can analyze database performance data and predict future performance. This improves the accuracy of anomaly detection by applying an appropriate detection algorithm to a specific component. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input data of a specific component to a generation AI and cause the generation AI to apply a detection algorithm.

[0084] The detection unit can adjust the accuracy of anomaly detection according to the system load status. For example, when the system load is high, the detection unit performs detailed anomaly detection to aim for early detection of anomalies. For example, when the system load is high, the detection unit can collect detailed data and detect anomalies early. The detection unit can also reduce the level of detail of anomaly detection to save resources when the system load is low. For example, when the system load is low, the detection unit can reduce the frequency of anomaly detection to save resources. Furthermore, the detection unit can adjust the level of detail of anomaly detection in real time according to load fluctuations. For example, the detection unit can monitor the system load status in real time and adjust the level of detail of anomaly detection according to load fluctuations. This enables efficient use of resources by adjusting the level of detail of anomaly detection according to the system load status. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input system load data to a generation AI and cause the generation AI to adjust the level of detail of anomaly detection.

[0085] The detection unit can estimate the user's emotions and adjust the display method of the anomaly detection results based on the user's emotions. For example, if the user is nervous, the detection unit can provide a simple, highly visible display method. For example, the detection unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide a simple display method. Furthermore, if the user is relaxed, the detection unit can provide a display method that includes detailed information. For example, the detection unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a detailed display method. Furthermore, if the user is in a hurry, the detection unit can provide a display method that focuses on the key points. For example, the detection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and provide a display method that focuses on the key points. This allows the display method of the anomaly detection results to be adjusted according to the user's emotions, making it easier for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI, or may be performed without using AI. For example, the detection unit may input user emotion data to the generation AI and cause the generation AI to adjust the display method of the anomaly detection result.

[0086] The detection unit can determine the anomaly detection range based on the geographical distribution of the system. For example, for a geographically distributed system, the detection unit can set a different anomaly detection range for each region. For example, for geographically distributed data centers, the detection unit can set a different anomaly detection range for each region. The detection unit can also perform anomaly detection with a focus on geographically important locations. For example, the detection unit can perform anomaly detection with a focus on geographically important locations. Furthermore, the detection unit can determine the priority of anomaly detection based on the geographical distribution. For example, the detection unit can determine the priority of anomaly detection based on the geographical distribution and prioritize anomaly detection in important regions. This enables effective anomaly detection by taking the geographical distribution into consideration. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the geographical distribution data of the system to the generation AI and cause the generation AI to determine the anomaly detection range.

[0087] The detection unit can improve the accuracy of anomaly detection based on related literature of the system. The detection unit, for example, refers to the latest research papers to improve the anomaly detection algorithm. For example, the detection unit can refer to the latest research papers to improve the anomaly detection algorithm. The detection unit can also improve the accuracy of anomaly detection based on related technical literature. For example, the detection unit can improve the anomaly detection algorithm based on related technical literature. Furthermore, the detection unit can improve the accuracy of anomaly detection by referring to past cases. For example, the detection unit can improve the anomaly detection algorithm by referring to past cases. As a result, the accuracy of anomaly detection is improved by referring to related literature. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input related literature data to the generation AI and cause the generation AI to improve the anomaly detection algorithm.

[0088] The detection unit can determine the priority of anomaly detection based on the market value of the system. The detection unit, for example, prioritizes anomaly detection for high-value systems. For example, the detection unit can evaluate the market value of the system and prioritize anomaly detection for high-value systems. The detection unit can also focus anomaly detection on components with high market value. For example, the detection unit can focus anomaly detection on components with high market value. Furthermore, the detection unit can adjust the priority of anomaly detection in real time in accordance with fluctuations in market value. For example, the detection unit can adjust the priority of anomaly detection in real time in accordance with fluctuations in market value and prioritize anomaly detection for important systems. This allows anomaly detection to be prioritized for important systems by taking market value into consideration. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input market value data of the system to the generation AI and cause the generation AI to determine the priority of anomaly detection.

[0089] The countermeasure unit can estimate the user's emotions and adjust the countermeasure selection method based on the user's emotions. For example, if the user is feeling stressed, the countermeasure unit prioritizes quick and easy countermeasures. For example, the countermeasure unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and prioritize quick and easy countermeasures. Furthermore, if the user is relaxed, the countermeasure unit can suggest detailed countermeasures. For example, the countermeasure unit can record the user's voice, estimate the user's emotions using voice analysis technology, and suggest detailed countermeasures. Furthermore, if the user is in a hurry, the countermeasure unit can quickly implement the most effective countermeasure. For example, the countermeasure unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and quickly implement the most effective countermeasure. This enables appropriate countermeasures to be implemented by adjusting the countermeasure selection method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit may input user emotion data into the generation AI and have the generation AI adjust the countermeasure selection method.

[0090] The countermeasure unit can select appropriate countermeasures based on the system's past countermeasure data. For example, the countermeasure unit can prioritize selecting countermeasures that were effective in the past. For example, the countermeasure unit can analyze past countermeasure data and prioritize selecting countermeasures that were effective. The countermeasure unit can also propose optimal countermeasures based on past countermeasure data. For example, the countermeasure unit can analyze past countermeasure data and propose optimal countermeasures. Furthermore, the countermeasure unit can analyze past countermeasure history and select the most effective countermeasure. For example, the countermeasure unit can analyze past countermeasure history and select the most effective countermeasure. This makes it possible to implement effective countermeasures by referring to past countermeasure data. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input past countermeasure data into a generation AI and have the generation AI select appropriate countermeasures.

[0091] The countermeasure unit can apply individual countermeasure algorithms to specific components of the system. For example, the countermeasure unit applies a restart algorithm to a server abnormality. For example, the countermeasure unit can apply a restart algorithm when a server abnormality is detected. The countermeasure unit can also apply a configuration change algorithm to a network abnormality. For example, the countermeasure unit can apply a configuration change algorithm when a network abnormality is detected. Furthermore, the countermeasure unit can apply a data repair algorithm to a database abnormality. For example, the countermeasure unit can apply a data repair algorithm when a database abnormality is detected. This improves the accuracy of countermeasures by applying an appropriate countermeasure algorithm to a specific component. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input data of a specific component to a generation AI and have the generation AI apply a countermeasure algorithm.

[0092] The countermeasure unit can adjust the accuracy of the countermeasures according to the system load status. For example, when the system load is high, the countermeasure unit can prioritize quick and simple countermeasures. For example, when the system load is high, the countermeasure unit can prioritize quick and simple countermeasures. Furthermore, the countermeasure unit can also implement detailed countermeasures when the system load is low. For example, when the system load is low, the countermeasure unit can implement detailed countermeasures. Furthermore, the countermeasure unit can adjust the level of detail of the countermeasures in real time according to load fluctuations. For example, the countermeasure unit can monitor the system load status in real time and adjust the level of detail of the countermeasures according to load fluctuations. This enables efficient use of resources by adjusting the level of detail of the countermeasures according to the system load status. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input system load data to a generation AI and have the generation AI adjust the level of detail of the countermeasures.

[0093] The countermeasure unit can estimate the user's emotions and adjust the display method of the countermeasure results based on the user's emotions. For example, if the user is nervous, the countermeasure unit can provide a simple, highly visible display method. For example, the countermeasure unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and provide a simple display method. Furthermore, if the user is relaxed, the countermeasure unit can provide a display method that includes detailed information. For example, the countermeasure unit can record the user's voice, estimate the emotion using voice analysis technology, and provide a detailed display method. Furthermore, if the user is in a hurry, the countermeasure unit can provide a display method that focuses on the main points. For example, the countermeasure unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the emotion using an emotion estimation algorithm, and provide a display method that focuses on the main points. This allows the display method of the countermeasure results to be adjusted according to the user's emotions, thereby enabling a display that is easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the countermeasure unit may be performed using AI, or may be performed without using AI. For example, the countermeasure unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method of the countermeasure results.

[0094] The countermeasures department can determine the scope of countermeasures based on the geographical distribution of the system. For example, for a geographically distributed system, the countermeasures department can set a different countermeasure scope for each region. For example, for a geographically distributed data center, the countermeasures department can set a different countermeasure scope for each region. The countermeasures department can also implement countermeasures intensively for geographically important bases. For example, the countermeasures department can implement countermeasures intensively for geographically important bases. Furthermore, the countermeasures department can determine the priority of countermeasures based on the geographical distribution. For example, the countermeasures department can determine the priority of countermeasures based on the geographical distribution and prioritize countermeasures in important regions. This enables effective countermeasures by taking the geographical distribution into consideration. Some or all of the above-described processing in the countermeasures department may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasures department can input the geographical distribution data of the system into a generation AI and have the generation AI determine the scope of countermeasures.

[0095] The countermeasure unit can improve the accuracy of the countermeasures based on related literature of the system. The countermeasure unit, for example, refers to the latest research papers to improve the countermeasure algorithm. For example, the countermeasure unit can refer to the latest research papers to improve the countermeasure algorithm. The countermeasure unit can also improve the accuracy of the countermeasures based on related technical literature. For example, the countermeasure unit can improve the countermeasure algorithm based on related technical literature. Furthermore, the countermeasure unit can improve the accuracy of the countermeasures by referring to past cases. For example, the countermeasure unit can improve the countermeasure algorithm by referring to past cases. As a result, the accuracy of the countermeasures is improved by referring to related literature. Some or all of the above-mentioned processing in the countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the countermeasure unit can input related literature data into the generation AI and have the generation AI improve the countermeasure algorithm.

[0096] The countermeasures department can determine the priority of countermeasures based on the market value of the system. The countermeasures department, for example, prioritizes countermeasures for high-value systems. For example, the countermeasures department can evaluate the market value of the system and prioritize countermeasures for high-value systems. The countermeasures department can also prioritize countermeasures for components with high market value. For example, the countermeasures department can prioritize countermeasures for components with high market value. Furthermore, the countermeasures department can adjust the priority of countermeasures in real time in accordance with fluctuations in market value. For example, the countermeasures department can adjust the priority of countermeasures in real time in accordance with fluctuations in market value and prioritize countermeasures for important systems. This allows countermeasures to be prioritized for important systems by taking market value into consideration. Some or all of the above-mentioned processing in the countermeasures department may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasures department can input market value data of the system into a generation AI and have the generation AI determine the priority of countermeasures.

[0097] The optimization unit can estimate the user's emotions and adjust the optimization method based on the user's emotions. For example, if the user is feeling stressed, the optimization unit prioritizes a quick and easy optimization method. For example, the optimization unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and prioritize a quick and easy optimization method. The optimization unit can also suggest a more detailed optimization method if the user is relaxed. For example, the optimization unit can record the user's voice, estimate the user's emotions using voice analysis technology, and suggest a more detailed optimization method. Furthermore, if the user is in a hurry, the optimization unit can quickly execute the most effective optimization method. For example, the optimization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and quickly execute the most effective optimization method. This enables appropriate optimization by adjusting the optimization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit may input user emotion data to the generation AI and cause the generation AI to adjust the optimization method.

[0098] The optimization unit can propose appropriate settings based on past performance data of the system. For example, the optimization unit prioritizes the proposal of settings that were effective in the past. For example, the optimization unit can analyze past performance data and prioritize the proposal of settings that were effective. The optimization unit can also propose optimal settings based on past performance data. For example, the optimization unit can analyze past performance data and propose optimal settings. Furthermore, the optimization unit can analyze past performance history and propose the most effective settings. For example, the optimization unit can analyze past performance history and propose the most effective settings. This enables effective optimization by referring to past performance data. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input past performance data to a generation AI and cause the generation AI to propose appropriate settings.

[0099] The optimization unit can apply individual optimization algorithms to specific components of the system. For example, the optimization unit can apply a real-time optimization algorithm to server resource allocation. For example, the optimization unit can apply an algorithm that optimizes server resource allocation in real time. The optimization unit can also apply an optimization algorithm to network bandwidth. For example, the optimization unit can apply an algorithm that optimizes network bandwidth. Furthermore, the optimization unit can apply an optimization algorithm based on historical data to database query performance. For example, the optimization unit can apply an algorithm that optimizes database query performance. This improves optimization accuracy by applying an appropriate optimization algorithm to a specific component. Some or all of the above-mentioned processing in the optimization unit may be performed using, or without, AI. For example, the optimization unit can input data of a specific component to a generation AI and cause the generation AI to apply an optimization algorithm.

[0100] The optimization unit can adjust the accuracy of optimization according to the system load status. For example, when the system load is high, the optimization unit can prioritize quick and simple optimization. For example, when the system load is high, the optimization unit can prioritize quick and simple optimization. The optimization unit can also perform detailed optimization when the system load is low. For example, when the system load is low, the optimization unit can perform detailed optimization. Furthermore, the optimization unit can adjust the level of detail of optimization in real time according to load fluctuations. For example, the optimization unit can monitor the system load status in real time and adjust the level of detail of optimization according to load fluctuations. This enables efficient use of resources by adjusting the level of detail of optimization according to the system load status. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input system load data to the generation AI and cause the generation AI to adjust the level of detail of optimization.

[0101] The optimization unit can estimate the user's emotions and adjust the display method of the optimization results based on the user's emotions. For example, if the user is nervous, the optimization unit can provide a simple, highly visible display method. For example, the optimization unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide a simple display method. Furthermore, if the user is relaxed, the optimization unit can provide a display method that includes detailed information. For example, the optimization unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a detailed display method. Furthermore, if the user is in a hurry, the optimization unit can provide a display method that focuses on the main points. For example, the optimization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and provide a display method that focuses on the main points. This allows the display method of the optimization results to be adjusted according to the user's emotions, resulting in a display that is easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the optimization unit may be performed using AI, or may be performed without using AI. For example, the optimization unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method of the optimization results.

[0102] The optimization unit can determine the optimization range based on the geographical distribution of the system. For example, for a geographically distributed system, the optimization unit can set a different optimization range for each region. For example, for geographically distributed data centers, the optimization unit can set a different optimization range for each region. The optimization unit can also perform focused optimization for geographically important locations. For example, the optimization unit can perform focused optimization for geographically important locations. Furthermore, the optimization unit can determine optimization priorities based on the geographical distribution. For example, the optimization unit can determine optimization priorities based on the geographical distribution and prioritize optimization for important regions. This enables effective optimization by taking geographical distribution into consideration. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input geographical distribution data of the system to the generation AI and cause the generation AI to determine the optimization range.

[0103] The optimization unit can improve the accuracy of optimization based on related literature of the system. The optimization unit, for example, refers to the latest research papers to improve the optimization algorithm. For example, the optimization unit can refer to the latest research papers to improve the optimization algorithm. The optimization unit can also improve the accuracy of optimization based on related technical literature. For example, the optimization unit can improve the optimization algorithm based on related technical literature. Furthermore, the optimization unit can improve the accuracy of optimization by referring to past cases. For example, the optimization unit can improve the optimization algorithm by referring to past cases. As a result, the accuracy of optimization is improved by referring to related literature. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input related literature data into the generation AI and cause the generation AI to improve the optimization algorithm.

[0104] The optimization unit can determine optimization priorities based on the market value of the system. The optimization unit, for example, prioritizes optimization of high-value systems. For example, the optimization unit can evaluate the market value of the system and prioritize optimization of high-value systems. The optimization unit can also prioritize optimization of components with high market value. For example, the optimization unit can prioritize optimization of components with high market value. Furthermore, the optimization unit can adjust the optimization priorities in real time in accordance with fluctuations in market value. For example, the optimization unit can adjust the optimization priorities in real time in accordance with fluctuations in market value and prioritize optimization of important systems. This allows important systems to be prioritized for optimization by taking market value into consideration. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input market value data of the system to the generation AI and cause the generation AI to determine the optimization priorities. === Hard Collateral 1-1 === Each of the multiple elements, including the monitoring unit, detection unit, countermeasure unit, and optimization unit, described above, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit can monitor the physical state of the system using the camera 42 or sensors of the smart device 14, and the collected data can be analyzed by the identification processing unit 290 of the data processing device 12. For example, the detection unit can identify an abnormality using a machine learning algorithm by the identification processing unit 290 of the data processing device 12, and identify the type of abnormality by the control unit 46A of the smart device 14. For example, the countermeasure unit can select an appropriate countermeasure using a rule-based system by the identification processing unit 290 of the data processing device 12, and automatically execute the countermeasure by the control unit 46A of the smart device 14. For example, the optimization unit can calculate resource allocation using a data analysis algorithm by the identification processing unit 290 of the data processing device 12, and propose optimal settings by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the monitoring unit, detection unit, countermeasure unit, and optimization unit, described above, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit can monitor the physical state of the system using the camera 42 or a sensor of the smart glasses 214, and analyze the collected data using the specific processing unit 290 of the data processing device 12. For example, the detection unit can identify an anomaly using a machine learning algorithm using the specific processing unit 290 of the data processing device 12, and identify the type of the anomaly using the control unit 46A of the smart glasses 214. For example, the countermeasure unit can select an appropriate countermeasure using a rule-based system using the specific processing unit 290 of the data processing device 12, and automatically execute the countermeasure using the control unit 46A of the smart glasses 214. For example, the optimization unit can calculate resource allocation using a data analysis algorithm using the specific processing unit 290 of the data processing device 12, and suggest optimal settings using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the monitoring unit, detection unit, countermeasure unit, and optimization unit described above is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the monitoring unit can monitor the physical state of the system using the camera 42 or a sensor of the headset type terminal 314, and the collected data can be analyzed by the identification processing unit 290 of the data processing device 12. For example, the detection unit can identify an abnormality using a machine learning algorithm by the identification processing unit 290 of the data processing device 12, and identify the type of abnormality by the control unit 46A of the headset type terminal 314. For example, the countermeasure unit can select an appropriate countermeasure using a rule-based system by the identification processing unit 290 of the data processing device 12, and automatically execute the countermeasure by the control unit 46A of the headset type terminal 314. For example, the optimization unit can calculate resource allocation using a data analysis algorithm by the identification processing unit 290 of the data processing device 12, and propose optimal settings by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the monitoring unit, detection unit, countermeasure unit, and optimization unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit can monitor the physical state of the system using the camera 42 or sensors of the robot 414 and analyze the collected data by the specific processing unit 290 of the data processing device 12. For example, the detection unit can identify an abnormality using a machine learning algorithm by the specific processing unit 290 of the data processing device 12, and identify the type of abnormality by the control unit 46A of the robot 414. For example, the countermeasure unit can select an appropriate countermeasure using a rule-based system by the specific processing unit 290 of the data processing device 12, and automatically execute the countermeasure by the control unit 46A of the robot 414. For example, the optimization unit can calculate resource allocation using a data analysis algorithm by the specific processing unit 290 of the data processing device 12, and propose optimal settings by the control unit 46A of the robot 414.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The monitoring unit can monitor the energy consumption of the system and optimize energy efficiency. For example, the monitoring unit can collect energy consumption data for each component and identify areas with low energy efficiency. The monitoring unit can also identify peak energy consumption times and propose measures to level out energy consumption. Furthermore, the monitoring unit can analyze the energy consumption data and propose optimal settings to improve energy efficiency. This can improve the energy efficiency of the system.

[0107] The detection unit can detect system security threats and issue alerts in real time. For example, the detection unit can detect abnormal login attempts and signs of unauthorized access. The detection unit can also monitor malware activity and detect signs of infection early. Furthermore, the detection unit can analyze network traffic and detect signs of DDoS attacks. This strengthens system security and enables rapid response.

[0108] The countermeasures department can perform preventive maintenance to improve system performance. For example, the countermeasures department can automatically schedule regular hardware inspections and software updates. The countermeasures department can also analyze system performance data, identify potential problems in advance, and implement preventative measures. Furthermore, the countermeasures department can suggest optimal maintenance schedules based on system usage. This helps minimize system downtime and improve performance.

[0109] The optimization unit can optimize performance by dynamically allocating system resources. For example, the optimization unit can monitor resource usage in real time and reallocate resources as needed. The optimization unit can also allocate resources preferentially to specific tasks or applications. Furthermore, the optimization unit can dynamically adjust resource allocation according to the system load status. This maximizes system performance and enables efficient resource utilization.

[0110] The monitoring unit can estimate the user's emotions and adjust the priority of alerts based on the user's emotions. For example, if the user is feeling stressed, the monitoring unit can prioritize displaying important alerts. Also, if the user is relaxed, the monitoring unit can reduce the frequency of alert display. Furthermore, if the user is in a hurry, the monitoring unit can quickly display alerts with high urgency. This makes it possible to provide appropriate information by adjusting the priority of alerts according to the user's emotions.

[0111] The detection unit can estimate the user's emotions and adjust the anomaly detection alert method based on the user's emotions. For example, if the user is nervous, the detection unit can provide a simple, highly visible alert. If the user is relaxed, the detection unit can also provide an alert with detailed information. Furthermore, if the user is in a hurry, the detection unit can provide an alert that focuses on the main points. In this way, by adjusting the anomaly detection alert method according to the user's emotions, it is possible to provide alerts that are easy for the user to see.

[0112] The countermeasure unit can estimate the user's emotions and adjust the timing of countermeasure execution based on the user's emotions. For example, the countermeasure unit can quickly execute a countermeasure when the user is feeling stressed. Also, the countermeasure unit can delay the execution of a countermeasure when the user is relaxed. Furthermore, the countermeasure unit can quickly execute the most effective countermeasure when the user is in a hurry. This makes it possible to take appropriate countermeasures by adjusting the execution timing of the countermeasures according to the user's emotions.

[0113] The optimization unit can estimate the user's emotions and adjust the priority of optimization based on the user's emotions. For example, if the user is feeling stressed, the optimization unit can prioritize the execution of important optimization. Also, if the user is relaxed, the optimization unit can delay the execution of optimization. Furthermore, if the user is in a hurry, the optimization unit can quickly execute optimization. In this way, by adjusting the priority of optimization according to the user's emotions, appropriate optimization is possible.

[0114] The monitoring unit can collect environmental data about the system and propose measures to minimize its impact on the environment. For example, the monitoring unit can collect temperature, humidity, and energy consumption data about the system and evaluate its impact on the environment. The monitoring unit can also analyze the environmental data and propose measures to improve energy efficiency. Furthermore, the monitoring unit can propose settings to optimize system operation based on the environmental data. This minimizes the system's impact on the environment and enables sustainable operation.

[0115] The detection unit can analyze system performance data and predict future anomalies. For example, the detection unit can predict the possibility of future anomalies based on past performance data. The detection unit can also propose preventive measures based on the results of the anomaly prediction. Furthermore, the detection unit can propose settings to optimize system operation based on the results of the anomaly prediction. This makes it possible to predict future anomalies and take measures in advance, thereby improving system stability.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The monitoring unit monitors the system. The monitoring unit collects various sensor and log data to monitor the system status. For example, it uses temperature and pressure sensors to monitor the physical status of the system, and collects access and error logs to monitor the system's operating status. It also collects data in real time to provide basic data for detecting abnormalities. Step 2: The detection unit detects anomalies based on the collected data. The detection unit uses machine learning algorithms and anomaly detection algorithms to identify anomalies, and a classification algorithm to identify the type of anomaly. The detection unit also analyzes the collected data and identifies the cause of the anomaly. Step 3: The countermeasures department troubleshoots the detected anomalies. The countermeasures department uses a rule-based system or rule engine to select appropriate countermeasures and automatically executes countermeasures against the anomaly using a rule set. The appropriate countermeasure can also be selected depending on the type of anomaly. Step 4: The optimization unit optimizes the system based on the implemented measures. The optimization unit uses data analysis algorithms to calculate resource allocation and uses regression analysis and clustering to optimize the system's resource allocation. It also analyzes system performance data and proposes optimal settings.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] 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.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] 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.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0181] 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.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 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 monitoring unit that monitors the system; a detection unit that detects an abnormality based on the data collected by the monitoring unit; a countermeasure unit that performs troubleshooting for the abnormality detected by the detection unit; an optimization unit that optimizes the system based on the countermeasures taken by the countermeasure unit; A system characterized by:

2. The monitoring unit Collecting various sensor or log data 2. The system of claim 1.

3. The detection unit Use machine learning algorithms to identify anomalies 2. The system of claim 1.

4. The countermeasure unit Use a rules-based system to select the appropriate countermeasure 2. The system of claim 1.

5. The optimization unit Calculate resource allocation using data analysis algorithms 2. The system of claim 1.

6. The monitoring unit Estimate user emotions and adjust monitoring frequency based on user emotions 2. The system of claim 1.

7. The monitoring unit Determine monitoring priorities based on historical system performance data 2. The system of claim 1.

8. The monitoring unit Applying individual monitoring algorithms to specific components of the system 2. The system of claim 1.

9. The monitoring unit Adjust the accuracy of monitoring according to the system load 2. The system of claim 1.

10. The monitoring unit Inferring user emotions and adjusting the display method of monitoring results based on the user emotions 2. The system of claim 1.

Images