system

Abstract

The system according to this embodiment aims to predict equipment failures in advance and replace them at the appropriate time. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a prediction unit, and a replacement unit. The collection unit collects syslog, trap, and cip level information from each device. The analysis unit analyzes the information collected by the collection unit and evaluates the status of the equipment. The prediction unit predicts the occurrence of equipment failure based on the analysis results obtained by the analysis unit. The replacement unit performs replacement in advance based on the equipment failure predicted by the prediction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to predict a failure of a device in advance and perform replacement at an appropriate timing.

[0005] <于 The system according to the embodiment aims to predict a failure of a device in advance and perform replacement at an appropriate timing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, and a replacement unit. The data collection unit collects syslog, trap, and cip level information from each device. The analysis unit analyzes the information collected by the data collection unit and evaluates the status of the equipment. The prediction unit predicts the occurrence of equipment failure based on the analysis results obtained by the analysis unit. The replacement unit performs replacement in advance based on the equipment failure predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict equipment failures in advance and replace them at the appropriate time. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The equipment failure prediction system according to an embodiment of the present invention analyzes information obtained not only from syslog and traps that can be viewed by operators of each device in the SB network, but also from CIP-level information of each device used for vendor analysis, in order to predict the occurrence of equipment failures and perform replacements in advance. This system collects syslog, trap, and CIP-level information from each device, analyzes the collected information to evaluate the status of the equipment, predicts the occurrence of equipment failures based on the analysis results, and performs replacements in advance as necessary. For example, it collects syslog, trap, and CIP-level information from each device. At this time, information is collected periodically in order to understand the status of each device in detail. For example, information such as CPU usage, memory usage, and temperature of each device is collected. This makes it possible to monitor the status of each device in real time. Next, the collected information is analyzed to evaluate the status of the equipment. AI can be used for the analysis. The AI ​​evaluates the status of the equipment based on the collected information and determines whether an abnormality has occurred. For example, it can issue a warning if there is a possibility of an abnormality, such as if the CPU usage is abnormally high or the temperature is abnormally high. Furthermore, it predicts the occurrence of equipment failures based on the analysis results and performs replacements in advance as necessary. The AI ​​can predict the occurrence of equipment failures based on past data. For example, if equipment failure occurred in the past when a similar condition occurred, it can be predicted that equipment failure is highly likely to occur again if that condition occurs. This allows for proactive replacement before equipment failure occurs. This system can prevent service interruptions due to equipment failure. For example, in a communication network, equipment failure can disrupt communication, but this system allows for proactive replacement before equipment failure occurs, thus preventing communication interruptions. Furthermore, proactive replacement before equipment failure occurs reduces the time and cost associated with repairs and replacements. In this way, the equipment failure prediction system can prevent service interruptions due to equipment failure.

[0029] The equipment failure prediction system according to the embodiment comprises a collection unit, an analysis unit, a prediction unit, and a replacement unit. The collection unit collects syslog, trap, and CIP-level information from each device. The collection unit collects information such as CPU usage, memory usage, and temperature for each device. For example, the collection unit periodically collects syslog from each device to obtain error logs and warning messages. The collection unit can also collect traps from each device in real time to obtain status information. Furthermore, the collection unit can collect CIP-level information for each device to understand its detailed status. For example, the collection unit periodically monitors the CPU usage of each device to detect abnormal usage. The collection unit can also monitor the memory usage of each device to detect anomalies such as memory leaks. The collection unit can also measure the temperature of each device to detect overheating. The analysis unit analyzes the information collected by the collection unit to evaluate the status of the equipment. For example, the analysis unit uses AI to analyze the collected information and evaluate the status of the equipment. For example, the analysis unit uses deep learning to analyze the collected information and detect anomalies. The analysis unit can also evaluate the status of the equipment using a neural network. The analysis unit can, for example, execute an anomaly detection algorithm based on the collected information to detect anomalies. The analysis unit can also evaluate the status of the equipment based on the collected information and determine whether it is in a normal or abnormal state. The prediction unit predicts the occurrence of equipment failure based on the analysis results obtained by the analysis unit. The prediction unit can, for example, use a machine learning model that predicts the occurrence of equipment failure based on past data. The prediction unit can, for example, use a regression model to predict the occurrence of equipment failure. The prediction unit can also predict the occurrence of equipment failure using a classification model. The prediction unit can, for example, calculate the probability of equipment failure occurring based on past data and evaluate the risk of failure. The prediction unit can also predict the timing of equipment failure based on past data. The replacement unit performs replacements in advance based on the equipment failures predicted by the prediction unit. The replacement unit can, for example, perform replacement work before the equipment failures predicted by the prediction unit occur. The replacement unit can, for example, prioritize replacing equipment with a high predicted failure risk. The replacement unit can also plan replacement work based on the predicted failure timing.As a result, the equipment failure prediction system according to the embodiment can prevent service interruptions due to equipment failure. Some or all of the above-described processes in the replacement unit may be performed using AI, for example, or without AI. For example, the replacement unit can plan replacement work using an AI model that takes the failure risk predicted by the prediction unit as input and outputs the timing of replacement work. For example, the output unit displays the replacement work plan through a web application or a mobile application. If feedback on paper is desired, the plan can be printed using a printer. Sending by email provides quick feedback by sending the plan directly to the person in charge of the work. Some or all of the above-described processes in the output unit may be performed using AI, for example, or without AI.

[0030] The data collection unit can collect information such as CPU usage, memory usage, and temperature for each device. For example, the data collection unit can periodically monitor the CPU usage of each device and detect abnormal usage. For example, the data collection unit can issue a warning if the CPU usage reaches 80% or higher. The data collection unit can also monitor the memory usage of each device and detect abnormalities such as memory leaks. For example, the data collection unit can issue a warning if the memory usage reaches 90% or higher. The data collection unit can also measure the temperature of each device and detect overheating. For example, the data collection unit can issue a warning if the temperature reaches 70 degrees or higher. This allows for a detailed understanding of the status of each device. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information such as the CPU usage, memory usage, and temperature of each device into a generating AI and have the generating AI perform abnormality detection.

[0031] The analysis unit can analyze the collected information using AI and evaluate the status of the equipment. For example, the analysis unit can analyze the collected information using deep learning and detect anomalies. For example, the analysis unit can use a deep learning model to learn anomaly patterns from the collected information and detect anomalies. The analysis unit can also evaluate the status of the equipment using a neural network. For example, the analysis unit can use a neural network model to evaluate the status of the equipment from the collected information and determine whether it is in a normal or abnormal state. The analysis unit can also execute an anomaly detection algorithm based on the collected information and detect anomalies. For example, the analysis unit can use an anomaly detection algorithm to detect anomalies from the collected information and issue a warning. This improves the accuracy of equipment status evaluation by using AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected information into a generative AI and have the generative AI perform anomaly detection.

[0032] The prediction unit can use a machine learning model that predicts the occurrence of equipment failures based on past data. For example, the prediction unit can use a regression model to predict the occurrence of equipment failures. For example, the prediction unit can learn a regression model based on past data and predict the probability of equipment failures occurring. The prediction unit can also use a classification model to predict the occurrence of equipment failures. For example, the prediction unit can learn a classification model based on past data and predict the timing of equipment failures occurring. The prediction unit can also calculate the probability of equipment failures occurring based on past data and evaluate the risk of failure. For example, the prediction unit can calculate the probability of equipment failures occurring based on past data and identify equipment with a high risk of failure. This makes it possible to predict the occurrence of equipment failures with high accuracy based on past data. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the prediction unit can input past data into a generative AI and have the generative AI perform the prediction of equipment failures.

[0033] The replacement unit can perform replacements in advance based on equipment failures predicted by the prediction unit. For example, the replacement unit can perform replacement work before the equipment failure predicted by the prediction unit occurs. For example, the replacement unit can prioritize replacing equipment with a high predicted failure risk. The replacement unit can also plan replacement work based on the predicted failure timing. For example, the replacement unit can create a replacement work schedule based on the predicted failure timing and perform replacement work efficiently. This prevents service interruptions by performing replacements in advance before equipment failures occur. Some or all of the above processes in the replacement unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the replacement unit can input the failure risk predicted by the prediction unit into the generation AI and have the generation AI execute the replacement work at the appropriate time.

[0034] The data collection unit can optimize the timing of information collection based on the operating time and environment of each device. For example, if the operating time of each device is long, the data collection unit can set the information collection timing to be shorter and monitor the status in real time. For example, the data collection unit can set the information collection frequency to every hour for devices with an operating time exceeding 1000 hours. The data collection unit can also set the information collection timing to be more frequent when the operating environment is harsh, enabling early detection of abnormalities. For example, the data collection unit can set the information collection frequency to every 30 minutes for devices operating in a high-temperature and high-humidity environment. The data collection unit can also set the information collection timing to be longer when the operating time is short, reducing the load on the system. For example, the data collection unit can set the information collection frequency to every day for devices with an operating time of less than 100 hours. This allows for early detection of abnormalities by optimizing the timing of information collection according to the operating time and environment of each device. Some or all of the above processing in the data collection unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the data collection unit can input operating time and usage environment data for each device into the generating AI, allowing the AI ​​to optimize the timing of information collection.

[0035] The data collection unit can customize the types of information it collects according to the type and application of the device. For example, in the case of a network device, the data collection unit can collect information on bandwidth and packet delay. For instance, the data collection unit can periodically monitor the bandwidth utilization rate of the network device and detect abnormal utilization rates. It can also monitor the packet delay time of the network device and issue a warning if a delay occurs. In the case of a server device, the data collection unit can also collect information on CPU utilization and memory utilization rates. For example, the data collection unit can periodically monitor the CPU utilization rate of the server device and detect abnormal utilization rates. It can also monitor the memory utilization rate of the server device and detect anomalies such as memory leaks. In the case of a sensor device, the data collection unit can also collect information on temperature and humidity. For example, the data collection unit can periodically measure the temperature of the sensor device and detect overheating. It can also monitor the humidity of the sensor device and detect abnormal humidity levels. This allows for more appropriate condition evaluation by collecting information according to the type and application of the device. Some or all of the above-described processing in the data collection unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the data collection unit can input information tailored to the type and purpose of the device into the generating AI, allowing the AI ​​to customize the data collection process.

[0036] The data collection unit can prioritize collecting information from devices in a specific region, taking into account the geographical location information of each device. For example, the data collection unit can prioritize collecting information from devices in geographically important regions. For example, the data collection unit can acquire the geographical location information of devices in geographically important regions and prioritize collecting information from devices in those regions. The data collection unit can also prioritize collecting information from devices in disaster-stricken areas. For example, the data collection unit can acquire the geographical location information of devices in disaster-stricken areas and prioritize collecting information from devices in those regions. The data collection unit can also prioritize collecting information from devices in high-traffic areas. For example, the data collection unit can acquire the geographical location information of devices in high-traffic areas and prioritize collecting information from devices in those regions. This allows for early detection of regional anomalies by prioritizing the collection of information from devices in specific regions. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the geographical location information of each device into a generative AI and have the generative AI determine the priority of information collection.

[0037] The data collection unit can collect information from social media and other external data sources and use it to evaluate the condition of the device. For example, the data collection unit can collect anomaly reports from social media and use them to evaluate the condition of the device. For example, the data collection unit can analyze social media posts and collect information about device anomalies. The data collection unit can also collect environmental information from other external data sources and use it to evaluate the condition of the device. For example, the data collection unit can collect weather data and environmental sensor data and use it to evaluate the condition of the device. The data collection unit can also collect traffic information from external data sources and use it to evaluate the condition of the device. For example, the data collection unit can collect network traffic data and use it to evaluate the condition of the device. This improves the accuracy of the device condition evaluation by collecting information from external data sources. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input information from social media and other external data sources into a generative AI and have the generative AI perform the device condition evaluation.

[0038] The analysis unit can improve the accuracy of its analysis by considering the device's operating history and maintenance history during the analysis. For example, the analysis unit can analyze the frequency of abnormalities by considering the device's operating history. For example, the analysis unit can evaluate the frequency of abnormalities based on the device's operating time and operating conditions. The analysis unit can also analyze the state after maintenance by considering the maintenance history. For example, the analysis unit can evaluate the state of the device after maintenance based on the maintenance history and predict the risk of abnormalities occurring. The analysis unit can also improve the accuracy of its analysis by combining the operating history and maintenance history. For example, the analysis unit can integrate the operating history and maintenance history to comprehensively evaluate the state of the device. This improves the accuracy of the analysis by considering the device's operating history and maintenance history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the device's operating history and maintenance history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0039] The analysis unit can optimize the analysis algorithm according to the type and application of the device. For example, the analysis unit can optimize the analysis algorithm for network devices. For example, the analysis unit can select the optimal analysis algorithm based on the characteristics of the network device to improve the accuracy of anomaly detection. The analysis unit can also optimize the analysis algorithm for server devices. For example, the analysis unit can select the optimal analysis algorithm based on the characteristics of the server device to improve the accuracy of anomaly detection. The analysis unit can also optimize the analysis algorithm for sensor devices. For example, the analysis unit can select the optimal analysis algorithm based on the characteristics of the sensor device to improve the accuracy of anomaly detection. As a result, analysis accuracy is improved by using an analysis algorithm appropriate to the type and application of the device. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input data appropriate to the type and application of the device into the generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0040] The analysis unit can classify analysis results by region, taking into account the geographical distribution of the devices during analysis. For example, the analysis unit can prioritize displaying analysis results for geographically important regions. For example, the analysis unit can acquire geographical location information of devices in geographically important regions and prioritize displaying analysis results for those regions. The analysis unit can also prioritize displaying analysis results for regions where disasters have occurred. For example, the analysis unit can acquire geographical location information of devices in regions where disasters have occurred and prioritize displaying analysis results for those regions. The analysis unit can also prioritize displaying analysis results for high-traffic regions. For example, the analysis unit can acquire geographical location information of devices in high-traffic regions and prioritize displaying analysis results for those regions. This allows for early detection of regional anomalies by considering the geographical distribution of the devices. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the geographical location information of the devices into a generating AI and have the generating AI perform the regional classification of the analysis results.

[0041] The analysis unit can improve the accuracy of its analysis by referring to relevant external data (e.g., weather data or market data) during the analysis. For example, the analysis unit can refer to weather data to analyze factors that affect the condition of the equipment. For example, the analysis unit can identify factors that affect the condition of the equipment based on weather data and improve the accuracy of its analysis. The analysis unit can also refer to market data to forecast the demand for the equipment. For example, the analysis unit can forecast the demand for the equipment based on market data and improve the accuracy of its analysis. The analysis unit can also refer to other external data to improve the accuracy of its analysis. For example, the analysis unit can refer to environmental data or economic data to help evaluate the condition of the equipment. In this way, the accuracy of the analysis is improved by referring to relevant external data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input relevant external data into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0042] The prediction unit can improve prediction accuracy by considering the device's past failure data and maintenance history during prediction. For example, the prediction unit can predict the probability of failure based on past failure data. For example, the prediction unit can calculate the probability of failure based on past failure data and evaluate the risk of failure. The prediction unit can also predict the probability of failure after maintenance based on maintenance history. For example, the prediction unit can calculate the probability of failure after maintenance based on maintenance history and evaluate the risk of failure. The prediction unit can also improve prediction accuracy by combining past failure data and maintenance history. For example, the prediction unit can integrate past failure data and maintenance history to comprehensively evaluate the probability of failure. This improves prediction accuracy by considering the device's past failure data and maintenance history. Some or all of the above processing in the prediction unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the prediction unit can input past failure data and maintenance history data into a generating AI and have the generating AI perform the prediction accuracy improvement.

[0043] The prediction unit can optimize the prediction algorithm according to the type and application of the device. For example, the prediction unit can optimize the prediction algorithm for network devices. For example, the prediction unit can select the optimal prediction algorithm based on the characteristics of the network device to improve the accuracy of fault prediction. The prediction unit can also optimize the prediction algorithm for server devices. For example, the prediction unit can select the optimal prediction algorithm based on the characteristics of the server device to improve the accuracy of fault prediction. The prediction unit can also optimize the prediction algorithm for sensor devices. For example, the prediction unit can select the optimal prediction algorithm based on the characteristics of the sensor device to improve the accuracy of fault prediction. As a result, prediction accuracy is improved by using a prediction algorithm appropriate to the type and application of the device. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the prediction unit can input data appropriate to the type and application of the device into the generative AI and have the generative AI perform the optimization of the prediction algorithm.

[0044] The prediction unit can classify prediction results by region, taking into account the geographical distribution of the devices during prediction. For example, the prediction unit can prioritize displaying prediction results for geographically important regions. For example, the prediction unit can acquire geographical location information of devices in geographically important regions and prioritize displaying prediction results for those regions. The prediction unit can also prioritize displaying prediction results for regions where disasters have occurred. For example, the prediction unit can acquire geographical location information of devices in regions where disasters have occurred and prioritize displaying prediction results for those regions. The prediction unit can also prioritize displaying prediction results for high-traffic regions. For example, the prediction unit can acquire geographical location information of devices in high-traffic regions and prioritize displaying prediction results for those regions. This allows for early detection of regional anomalies by considering the geographical distribution of the devices. Some or all of the above processing in the prediction unit may be performed using, for example, a generation AI, or without a generation AI. For example, the prediction unit can input the geographical location information of the devices into a generation AI and have the generation AI perform the regional classification of the prediction results.

[0045] The forecasting unit can improve its forecast accuracy by referring to relevant external data (e.g., weather data or market data) during forecasting. For example, the forecasting unit can refer to weather data to predict factors that affect the condition of the equipment. For example, the forecasting unit can identify factors that affect the condition of the equipment based on weather data and improve forecast accuracy. The forecasting unit can also refer to market data to forecast the demand for the equipment. For example, the forecasting unit can forecast the demand for the equipment based on market data and improve forecast accuracy. The forecasting unit can also refer to other external data to improve forecast accuracy. For example, the forecasting unit can refer to environmental data or economic data to help evaluate the condition of the equipment. In this way, forecast accuracy is improved by referring to relevant external data. Some or all of the above processing in the forecasting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the forecasting unit can input relevant external data into a generative AI and have the generative AI perform the forecast accuracy improvement.

[0046] The replacement unit can select the optimal replacement method at the time of replacement, taking into account the operating history and maintenance history of the equipment. For example, the replacement unit can select the optimal replacement method based on the operating history. For example, the replacement unit can select the optimal replacement method based on the operating time and operating status of the equipment. The replacement unit can also select the optimal replacement method based on the maintenance history. For example, the replacement unit can evaluate the condition of the equipment after maintenance based on the maintenance history and select the optimal replacement method. The replacement unit can also select the optimal replacement method by combining the operating history and maintenance history. For example, the replacement unit can integrate the operating history and maintenance history, comprehensively evaluate the condition of the equipment, and select the optimal replacement method. In this way, the optimal replacement method can be selected by taking into account the operating history and maintenance history of the equipment. Some or all of the above processing in the replacement unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the replacement unit can input the operating history and maintenance history data of the equipment into a generation AI and have the generation AI select the optimal replacement method.

[0047] The replacement unit can optimize replacement work according to the type and application of the equipment. For example, the replacement unit can optimize the replacement work of network equipment. For example, the replacement unit can select the optimal replacement procedure based on the characteristics of the network equipment, thereby improving the efficiency of the replacement work. The replacement unit can also optimize the replacement work of server equipment. For example, the replacement unit can select the optimal replacement procedure based on the characteristics of the server equipment, thereby improving the efficiency of the replacement work. The replacement unit can also optimize the replacement work of sensor equipment. For example, the replacement unit can select the optimal replacement procedure based on the characteristics of the sensor equipment, thereby improving the efficiency of the replacement work. As a result, work efficiency is improved by performing replacement work according to the type and application of the equipment. Some or all of the above processing in the replacement unit may be performed using, for example, a generation AI, or without a generation AI. For example, the replacement unit can input data according to the type and application of the equipment into the generation AI and have the generation AI perform the optimization of the replacement work.

[0048] The replacement unit can classify replacement work by region, taking into account the geographical distribution of the equipment during replacement. For example, the replacement unit can prioritize the replacement of equipment in geographically important regions. For example, the replacement unit can acquire geographical location information of equipment in geographically important regions and prioritize the replacement of equipment in those regions. The replacement unit can also prioritize the replacement of equipment in disaster-stricken areas. For example, the replacement unit can acquire geographical location information of equipment in disaster-stricken areas and prioritize the replacement of equipment in those regions. The replacement unit can also prioritize the replacement of equipment in high-traffic areas. For example, the replacement unit can acquire geographical location information of equipment in high-traffic areas and prioritize the replacement of equipment in those regions. This allows for early detection of regional anomalies by considering the geographical distribution of the equipment. Some or all of the above processing in the replacement unit may be performed using, for example, a generation AI, or without a generation AI. For example, the replacement unit can input the geographical location information of the equipment into a generation AI and have the generation AI perform the regional classification of replacement work.

[0049] The replacement unit can improve the efficiency of replacement work by referring to relevant external data (e.g., weather data or market data) during replacement. For example, the replacement unit can refer to weather data to determine the optimal timing for replacement work. For example, the replacement unit can identify the optimal timing for replacement work based on weather data and improve work efficiency. The replacement unit can also refer to market data to check the inventory status of replacement parts. For example, the replacement unit can evaluate the inventory status of replacement parts based on market data and determine the optimal replacement timing. The replacement unit can also improve the efficiency of replacement work by referring to other external data. For example, the replacement unit can refer to environmental data or economic data to identify the optimal timing for replacement work. In this way, the efficiency of replacement work is improved by referring to relevant external data. Some or all of the above processing in the replacement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the replacement unit can input relevant external data into a generative AI and have the generative AI perform the task of improving the efficiency of replacement work.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can optimize the timing of information collection based on the operating time and environment of each device. For example, if a device has a long operating time, the information collection timing can be set to be shorter to monitor its status in real time. For example, for a device with more than 1000 operating hours, the information collection frequency can be set to every hour. Also, if the operating environment is harsh, the information collection timing can be set to be more frequent to detect abnormalities early. For example, for a device operating in a high-temperature and high-humidity environment, the information collection frequency can be set to every 30 minutes. Furthermore, if the operating time is short, the information collection timing can be set to be longer to reduce the system load. For example, for a device with less than 100 operating hours, the information collection frequency can be set to every day. In this way, by optimizing the timing of information collection according to the operating time and environment of each device, abnormalities can be detected early.

[0052] The analysis unit can improve analysis accuracy by considering the device's operating history and maintenance history during the analysis process. For example, it can analyze the frequency of abnormalities by considering the device's operating history. For instance, it can evaluate the frequency of abnormalities based on the device's operating time and operating conditions. It can also analyze the state after maintenance by considering the maintenance history. For example, it can evaluate the state of the device after maintenance based on the maintenance history and predict the risk of abnormalities occurring. Furthermore, it can improve analysis accuracy by combining the operating history and maintenance history. For example, it can integrate the operating history and maintenance history to comprehensively evaluate the state of the device. In this way, considering the device's operating history and maintenance history improves analysis accuracy.

[0053] The prediction unit can improve prediction accuracy by considering the device's past failure data and maintenance history during the prediction process. For example, it can predict the probability of failure based on past failure data. For example, it can calculate the probability of failure based on past failure data and evaluate the risk of failure. It can also predict the probability of failure after maintenance based on maintenance history. For example, it can calculate the probability of failure after maintenance based on maintenance history and evaluate the risk of failure. Furthermore, it can improve prediction accuracy by combining past failure data and maintenance history. For example, it can integrate past failure data and maintenance history to comprehensively evaluate the probability of failure. In this way, prediction accuracy is improved by considering the device's past failure data and maintenance history.

[0054] The replacement unit can select the optimal replacement method by considering the device's operating history and maintenance history during replacement. For example, the optimal replacement method can be selected based on the operating history. For example, the optimal replacement method can be selected based on the device's operating time and operating status. It can also be selected based on the maintenance history. For example, the device's condition after maintenance can be evaluated based on the maintenance history, and the optimal replacement method can be selected. Furthermore, the optimal replacement method can be selected by combining the operating history and maintenance history. For example, the operating history and maintenance history can be integrated to comprehensively evaluate the device's condition and select the optimal replacement method. In this way, the optimal replacement method can be selected by considering the device's operating history and maintenance history.

[0055] The data collection unit can gather information from social media and other external data sources to help evaluate the condition of the device. For example, it can collect anomaly reports from social media and use them to evaluate the device's condition. For example, it can analyze social media posts to gather information about device anomalies. It can also collect environmental information from other external data sources and use it to evaluate the device's condition. For example, it can collect weather data and environmental sensor data and use it to evaluate the device's condition. It can also collect traffic information from external data sources and use it to evaluate the device's condition. For example, it can collect network traffic data and use it to evaluate the device's condition. As a result, the accuracy of the device's condition evaluation is improved by collecting information from external data sources.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection unit collects syslog, trap, and CIP-level information from each device. For example, it collects information such as CPU usage, memory usage, and temperature for each device, and periodically collects syslog to obtain error logs and warning messages. It can also collect traps in real time to obtain status information. Furthermore, it can collect CIP-level information to understand the detailed status. Step 2: The analysis unit analyzes the information collected by the collection unit and evaluates the status of the equipment. For example, it can use AI, deep learning, or neural networks to analyze the collected information and detect anomalies. It can also execute an anomaly detection algorithm based on the collected information to determine whether the state is normal or abnormal. Step 3: The prediction unit predicts the occurrence of equipment failures based on the analysis results obtained by the analysis unit. For example, it uses machine learning models, regression models, or classification models that predict the occurrence of equipment failures based on past data to predict the risk and timing of failures. Step 4: The replacement unit performs replacements in advance based on equipment failures predicted by the prediction unit. For example, it prioritizes replacing equipment with a high predicted failure risk and plans replacement work based on the timing of failures. The replacement work plan may also be displayed through a web application or mobile application.

[0058] (Example of form 2) The equipment failure prediction system according to an embodiment of the present invention analyzes information obtained not only from syslog and traps that can be viewed by operators of each device in the SB network, but also from CIP-level information of each device used for vendor analysis, in order to predict the occurrence of equipment failures and perform replacements in advance. This system collects syslog, trap, and CIP-level information from each device, analyzes the collected information to evaluate the status of the equipment, predicts the occurrence of equipment failures based on the analysis results, and performs replacements in advance as necessary. For example, it collects syslog, trap, and CIP-level information from each device. At this time, information is collected periodically in order to understand the status of each device in detail. For example, information such as CPU usage, memory usage, and temperature of each device is collected. This makes it possible to monitor the status of each device in real time. Next, the collected information is analyzed to evaluate the status of the equipment. AI can be used for the analysis. The AI ​​evaluates the status of the equipment based on the collected information and determines whether an abnormality has occurred. For example, it can issue a warning if there is a possibility of an abnormality, such as if the CPU usage is abnormally high or the temperature is abnormally high. Furthermore, it predicts the occurrence of equipment failures based on the analysis results and performs replacements in advance as necessary. The AI ​​can predict the occurrence of equipment failures based on past data. For example, if equipment failure occurred in the past when a similar condition occurred, it can be predicted that equipment failure is highly likely to occur again if that condition occurs. This allows for proactive replacement before equipment failure occurs. This system can prevent service interruptions due to equipment failure. For example, in a communication network, equipment failure can disrupt communication, but this system allows for proactive replacement before equipment failure occurs, thus preventing communication interruptions. Furthermore, proactive replacement before equipment failure occurs reduces the time and cost associated with repairs and replacements. In this way, the equipment failure prediction system can prevent service interruptions due to equipment failure.

[0059] The equipment failure prediction system according to the embodiment comprises a collection unit, an analysis unit, a prediction unit, and a replacement unit. The collection unit collects syslog, trap, and CIP-level information from each device. The collection unit collects information such as CPU usage, memory usage, and temperature for each device. For example, the collection unit periodically collects syslog from each device to obtain error logs and warning messages. The collection unit can also collect traps from each device in real time to obtain status information. Furthermore, the collection unit can collect CIP-level information for each device to understand its detailed status. For example, the collection unit periodically monitors the CPU usage of each device to detect abnormal usage. The collection unit can also monitor the memory usage of each device to detect anomalies such as memory leaks. The collection unit can also measure the temperature of each device to detect overheating. The analysis unit analyzes the information collected by the collection unit to evaluate the status of the equipment. For example, the analysis unit uses AI to analyze the collected information and evaluate the status of the equipment. For example, the analysis unit uses deep learning to analyze the collected information and detect anomalies. The analysis unit can also evaluate the status of the equipment using a neural network. The analysis unit can, for example, execute an anomaly detection algorithm based on the collected information to detect anomalies. The analysis unit can also evaluate the status of the equipment based on the collected information and determine whether it is in a normal or abnormal state. The prediction unit predicts the occurrence of equipment failure based on the analysis results obtained by the analysis unit. The prediction unit can, for example, use a machine learning model that predicts the occurrence of equipment failure based on past data. The prediction unit can, for example, use a regression model to predict the occurrence of equipment failure. The prediction unit can also predict the occurrence of equipment failure using a classification model. The prediction unit can, for example, calculate the probability of equipment failure occurring based on past data and evaluate the risk of failure. The prediction unit can also predict the timing of equipment failure based on past data. The replacement unit performs replacements in advance based on the equipment failures predicted by the prediction unit. The replacement unit can, for example, perform replacement work before the equipment failures predicted by the prediction unit occur. The replacement unit can, for example, prioritize replacing equipment with a high predicted failure risk. The replacement unit can also plan replacement work based on the predicted failure timing.As a result, the equipment failure prediction system according to the embodiment can prevent service interruptions due to equipment failure. Some or all of the above-described processes in the replacement unit may be performed using AI, for example, or without AI. For example, the replacement unit can plan replacement work using an AI model that takes the failure risk predicted by the prediction unit as input and outputs the timing of replacement work. For example, the output unit displays the replacement work plan through a web application or a mobile application. If feedback on paper is desired, the plan can be printed using a printer. Sending by email provides quick feedback by sending the plan directly to the person in charge of the work. Some or all of the above-described processes in the output unit may be performed using AI, for example, or without AI.

[0060] The data collection unit can collect information such as CPU usage, memory usage, and temperature for each device. For example, the data collection unit can periodically monitor the CPU usage of each device and detect abnormal usage. For example, the data collection unit can issue a warning if the CPU usage reaches 80% or higher. The data collection unit can also monitor the memory usage of each device and detect abnormalities such as memory leaks. For example, the data collection unit can issue a warning if the memory usage reaches 90% or higher. The data collection unit can also measure the temperature of each device and detect overheating. For example, the data collection unit can issue a warning if the temperature reaches 70 degrees or higher. This allows for a detailed understanding of the status of each device. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information such as the CPU usage, memory usage, and temperature of each device into a generating AI and have the generating AI perform abnormality detection.

[0061] The analysis unit can analyze the collected information using AI and evaluate the status of the equipment. For example, the analysis unit can analyze the collected information using deep learning and detect anomalies. For example, the analysis unit can use a deep learning model to learn anomaly patterns from the collected information and detect anomalies. The analysis unit can also evaluate the status of the equipment using a neural network. For example, the analysis unit can use a neural network model to evaluate the status of the equipment from the collected information and determine whether it is in a normal or abnormal state. The analysis unit can also execute an anomaly detection algorithm based on the collected information and detect anomalies. For example, the analysis unit can use an anomaly detection algorithm to detect anomalies from the collected information and issue a warning. This improves the accuracy of equipment status evaluation by using AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected information into a generative AI and have the generative AI perform anomaly detection.

[0062] The prediction unit can use a machine learning model that predicts the occurrence of equipment failures based on past data. For example, the prediction unit can use a regression model to predict the occurrence of equipment failures. For example, the prediction unit can learn a regression model based on past data and predict the probability of equipment failures occurring. The prediction unit can also use a classification model to predict the occurrence of equipment failures. For example, the prediction unit can learn a classification model based on past data and predict the timing of equipment failures occurring. The prediction unit can also calculate the probability of equipment failures occurring based on past data and evaluate the risk of failure. For example, the prediction unit can calculate the probability of equipment failures occurring based on past data and identify equipment with a high risk of failure. This makes it possible to predict the occurrence of equipment failures with high accuracy based on past data. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the prediction unit can input past data into a generative AI and have the generative AI perform the prediction of equipment failures.

[0063] The replacement unit can perform replacements in advance based on equipment failures predicted by the prediction unit. For example, the replacement unit can perform replacement work before the equipment failure predicted by the prediction unit occurs. For example, the replacement unit can prioritize replacing equipment with a high predicted failure risk. The replacement unit can also plan replacement work based on the predicted failure timing. For example, the replacement unit can create a replacement work schedule based on the predicted failure timing and perform replacement work efficiently. This prevents service interruptions by performing replacements in advance before equipment failures occur. Some or all of the above processes in the replacement unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the replacement unit can input the failure risk predicted by the prediction unit into the generation AI and have the generation AI execute the replacement work at the appropriate time.

[0064] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can lower the frequency of data collection to reduce the system load. For example, the data collection unit can use an emotion analysis algorithm to estimate the user's emotions and assess the user's stress level. The data collection unit can also set a higher frequency of data collection to collect more detailed data if the user is relaxed. For example, the data collection unit can use voice analysis or facial recognition technology to estimate the user's emotions and assess the user's level of relaxation. Furthermore, if the user is in a hurry, the data collection unit can optimize the frequency of data collection to quickly collect only the necessary data. For example, the data collection unit can use text analysis technology to estimate the user's emotions and assess the user's degree of urgency. This allows for system load optimization by adjusting the frequency of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using the generation AI, or not using the generation AI. For example, the collection unit can input user sentiment data into the generation AI and have the generation AI adjust the frequency of information collection.

[0065] The data collection unit can optimize the timing of information collection based on the operating time and environment of each device. For example, if the operating time of each device is long, the data collection unit can set the information collection timing to be shorter and monitor the status in real time. For example, the data collection unit can set the information collection frequency to every hour for devices with an operating time exceeding 1000 hours. The data collection unit can also set the information collection timing to be more frequent when the operating environment is harsh, enabling early detection of abnormalities. For example, the data collection unit can set the information collection frequency to every 30 minutes for devices operating in a high-temperature and high-humidity environment. The data collection unit can also set the information collection timing to be longer when the operating time is short, reducing the load on the system. For example, the data collection unit can set the information collection frequency to every day for devices with an operating time of less than 100 hours. This allows for early detection of abnormalities by optimizing the timing of information collection according to the operating time and environment of each device. Some or all of the above processing in the data collection unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the data collection unit can input operating time and usage environment data for each device into the generating AI, allowing the AI ​​to optimize the timing of information collection.

[0066] The data collection unit can customize the types of information it collects according to the type and application of the device. For example, in the case of a network device, the data collection unit can collect information on bandwidth and packet delay. For instance, the data collection unit can periodically monitor the bandwidth utilization rate of the network device and detect abnormal utilization rates. It can also monitor the packet delay time of the network device and issue a warning if a delay occurs. In the case of a server device, the data collection unit can also collect information on CPU utilization and memory utilization rates. For example, the data collection unit can periodically monitor the CPU utilization rate of the server device and detect abnormal utilization rates. It can also monitor the memory utilization rate of the server device and detect anomalies such as memory leaks. In the case of a sensor device, the data collection unit can also collect information on temperature and humidity. For example, the data collection unit can periodically measure the temperature of the sensor device and detect overheating. It can also monitor the humidity of the sensor device and detect abnormal humidity levels. This allows for more appropriate condition evaluation by collecting information according to the type and application of the device. Some or all of the above-described processing in the data collection unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the data collection unit can input information tailored to the type and purpose of the device into the generating AI, allowing the AI ​​to customize the data collection process.

[0067] The data collection unit can estimate the user's emotions and prioritize the information to collect based on those emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important information. For instance, it can use an emotion analysis algorithm to estimate the user's emotions and assess their stress level. Similarly, if the user is relaxed, the data collection unit can prioritize collecting detailed information. For example, it can use voice analysis or facial recognition technology to estimate the user's emotions and assess their level of relaxation. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information that can be quickly retrieved. For example, it can use text analysis technology to estimate the user's emotions and assess their degree of urgency. This allows for the prioritization of information based on the user's emotions, ensuring that important information is collected first. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information collection.

[0068] The data collection unit can prioritize collecting information from devices in a specific region, taking into account the geographical location information of each device. For example, the data collection unit can prioritize collecting information from devices in geographically important regions. For example, the data collection unit can acquire the geographical location information of devices in geographically important regions and prioritize collecting information from devices in those regions. The data collection unit can also prioritize collecting information from devices in disaster-stricken areas. For example, the data collection unit can acquire the geographical location information of devices in disaster-stricken areas and prioritize collecting information from devices in those regions. The data collection unit can also prioritize collecting information from devices in high-traffic areas. For example, the data collection unit can acquire the geographical location information of devices in high-traffic areas and prioritize collecting information from devices in those regions. This allows for early detection of regional anomalies by prioritizing the collection of information from devices in specific regions. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the geographical location information of each device into a generative AI and have the generative AI determine the priority of information collection.

[0069] The data collection unit can collect information from social media and other external data sources and use it to evaluate the condition of the device. For example, the data collection unit can collect anomaly reports from social media and use them to evaluate the condition of the device. For example, the data collection unit can analyze social media posts and collect information about device anomalies. The data collection unit can also collect environmental information from other external data sources and use it to evaluate the condition of the device. For example, the data collection unit can collect weather data and environmental sensor data and use it to evaluate the condition of the device. The data collection unit can also collect traffic information from external data sources and use it to evaluate the condition of the device. For example, the data collection unit can collect network traffic data and use it to evaluate the condition of the device. This improves the accuracy of the device condition evaluation by collecting information from external data sources. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input information from social media and other external data sources into a generative AI and have the generative AI perform the device condition evaluation.

[0070] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, the analysis unit can use an emotion analysis algorithm to estimate the user's emotions and evaluate the user's level of tension. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, the analysis unit can use voice analysis or facial recognition technology to estimate the user's emotions and evaluate the user's level of relaxation. The analysis unit can also provide a concise display method if the user is in a hurry. For example, the analysis unit can use text analysis technology to estimate the user's emotions and evaluate the user's degree of urgency. By adjusting the display method of the analysis results according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the display method of the analysis results.

[0071] The analysis unit can improve the accuracy of its analysis by considering the device's operating history and maintenance history during the analysis. For example, the analysis unit can analyze the frequency of abnormalities by considering the device's operating history. For example, the analysis unit can evaluate the frequency of abnormalities based on the device's operating time and operating conditions. The analysis unit can also analyze the state after maintenance by considering the maintenance history. For example, the analysis unit can evaluate the state of the device after maintenance based on the maintenance history and predict the risk of abnormalities occurring. The analysis unit can also improve the accuracy of its analysis by combining the operating history and maintenance history. For example, the analysis unit can integrate the operating history and maintenance history to comprehensively evaluate the state of the device. This improves the accuracy of the analysis by considering the device's operating history and maintenance history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the device's operating history and maintenance history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0072] The analysis unit can optimize the analysis algorithm according to the type and application of the device. For example, the analysis unit can optimize the analysis algorithm for network devices. For example, the analysis unit can select the optimal analysis algorithm based on the characteristics of the network device to improve the accuracy of anomaly detection. The analysis unit can also optimize the analysis algorithm for server devices. For example, the analysis unit can select the optimal analysis algorithm based on the characteristics of the server device to improve the accuracy of anomaly detection. The analysis unit can also optimize the analysis algorithm for sensor devices. For example, the analysis unit can select the optimal analysis algorithm based on the characteristics of the sensor device to improve the accuracy of anomaly detection. As a result, analysis accuracy is improved by using an analysis algorithm appropriate to the type and application of the device. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input data appropriate to the type and application of the device into the generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0073] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important analysis results. For instance, the analysis unit can use an emotion analysis algorithm to estimate the user's emotions and assess their stress level. The analysis unit can also prioritize displaying detailed analysis results if the user is relaxed. For example, the analysis unit can use voice analysis or facial recognition technology to estimate the user's emotions and assess their level of relaxation. Furthermore, the analysis unit can quickly display analysis results if the user is in a hurry. For example, the analysis unit can use text analysis technology to estimate the user's emotions and assess their degree of urgency. This allows for the prioritization of analysis results according to the user's emotions, thereby prioritizing the display of important information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform priority determination of the analysis results.

[0074] The analysis unit can classify analysis results by region, taking into account the geographical distribution of the devices during analysis. For example, the analysis unit can prioritize displaying analysis results for geographically important regions. For example, the analysis unit can acquire geographical location information of devices in geographically important regions and prioritize displaying analysis results for those regions. The analysis unit can also prioritize displaying analysis results for regions where disasters have occurred. For example, the analysis unit can acquire geographical location information of devices in regions where disasters have occurred and prioritize displaying analysis results for those regions. The analysis unit can also prioritize displaying analysis results for high-traffic regions. For example, the analysis unit can acquire geographical location information of devices in high-traffic regions and prioritize displaying analysis results for those regions. This allows for early detection of regional anomalies by considering the geographical distribution of the devices. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the geographical location information of the devices into a generating AI and have the generating AI perform the regional classification of the analysis results.

[0075] The analysis unit can improve the accuracy of its analysis by referring to relevant external data (e.g., weather data or market data) during the analysis. For example, the analysis unit can refer to weather data to analyze factors that affect the condition of the equipment. For example, the analysis unit can identify factors that affect the condition of the equipment based on weather data and improve the accuracy of its analysis. The analysis unit can also refer to market data to forecast the demand for the equipment. For example, the analysis unit can forecast the demand for the equipment based on market data and improve the accuracy of its analysis. The analysis unit can also refer to other external data to improve the accuracy of its analysis. For example, the analysis unit can refer to environmental data or economic data to help evaluate the condition of the equipment. In this way, the accuracy of the analysis is improved by referring to relevant external data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input relevant external data into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0076] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is tense, the prediction unit can provide a simple and highly visible display method. For example, the prediction unit can use an emotion analysis algorithm to estimate the user's emotions and assess the user's level of tension. The prediction unit can also provide a display method that includes detailed information if the user is relaxed. For example, the prediction unit can use voice analysis or facial recognition technology to estimate the user's emotions and assess the user's level of relaxation. The prediction unit can also provide a concise display method if the user is in a hurry. For example, the prediction unit can use text analysis technology to estimate the user's emotions and assess the user's degree of urgency. This improves visibility by adjusting the display method of the prediction results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the prediction unit can input user sentiment data into the generative AI and have the generative AI adjust the display method of the prediction results.

[0077] The prediction unit can improve prediction accuracy by considering the device's past failure data and maintenance history during prediction. For example, the prediction unit can predict the probability of failure based on past failure data. For example, the prediction unit can calculate the probability of failure based on past failure data and evaluate the risk of failure. The prediction unit can also predict the probability of failure after maintenance based on maintenance history. For example, the prediction unit can calculate the probability of failure after maintenance based on maintenance history and evaluate the risk of failure. The prediction unit can also improve prediction accuracy by combining past failure data and maintenance history. For example, the prediction unit can integrate past failure data and maintenance history to comprehensively evaluate the probability of failure. This improves prediction accuracy by considering the device's past failure data and maintenance history. Some or all of the above processing in the prediction unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the prediction unit can input past failure data and maintenance history data into a generating AI and have the generating AI perform the prediction accuracy improvement.

[0078] The prediction unit can optimize the prediction algorithm according to the type and application of the device. For example, the prediction unit can optimize the prediction algorithm for network devices. For example, the prediction unit can select the optimal prediction algorithm based on the characteristics of the network device to improve the accuracy of fault prediction. The prediction unit can also optimize the prediction algorithm for server devices. For example, the prediction unit can select the optimal prediction algorithm based on the characteristics of the server device to improve the accuracy of fault prediction. The prediction unit can also optimize the prediction algorithm for sensor devices. For example, the prediction unit can select the optimal prediction algorithm based on the characteristics of the sensor device to improve the accuracy of fault prediction. As a result, prediction accuracy is improved by using a prediction algorithm appropriate to the type and application of the device. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the prediction unit can input data appropriate to the type and application of the device into the generative AI and have the generative AI perform the optimization of the prediction algorithm.

[0079] The prediction unit can estimate the user's emotions and prioritize prediction results based on the estimated emotions. For example, if the user is stressed, the prediction unit will prioritize displaying important prediction results. For example, the prediction unit can use an emotion analysis algorithm to estimate the user's emotions and assess the user's stress level. The prediction unit can also prioritize displaying detailed prediction results if the user is relaxed. For example, the prediction unit can use voice analysis or facial recognition technology to estimate the user's emotions and assess the user's level of relaxation. The prediction unit can also quickly display prediction results if the user is in a hurry. For example, the prediction unit can use text analysis technology to estimate the user's emotions and assess the user's degree of urgency. This allows important information to be displayed preferentially by prioritizing prediction results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input user sentiment data into a generative AI and have the generative AI perform priority determination of the prediction results.

[0080] The prediction unit can classify prediction results by region, taking into account the geographical distribution of the devices during prediction. For example, the prediction unit can prioritize displaying prediction results for geographically important regions. For example, the prediction unit can acquire geographical location information of devices in geographically important regions and prioritize displaying prediction results for those regions. The prediction unit can also prioritize displaying prediction results for regions where disasters have occurred. For example, the prediction unit can acquire geographical location information of devices in regions where disasters have occurred and prioritize displaying prediction results for those regions. The prediction unit can also prioritize displaying prediction results for high-traffic regions. For example, the prediction unit can acquire geographical location information of devices in high-traffic regions and prioritize displaying prediction results for those regions. This allows for early detection of regional anomalies by considering the geographical distribution of the devices. Some or all of the above processing in the prediction unit may be performed using, for example, a generation AI, or without a generation AI. For example, the prediction unit can input the geographical location information of the devices into a generation AI and have the generation AI perform the regional classification of the prediction results.

[0081] The forecasting unit can improve its forecast accuracy by referring to relevant external data (e.g., weather data or market data) during forecasting. For example, the forecasting unit can refer to weather data to predict factors that affect the condition of the equipment. For example, the forecasting unit can identify factors that affect the condition of the equipment based on weather data and improve forecast accuracy. The forecasting unit can also refer to market data to forecast the demand for the equipment. For example, the forecasting unit can forecast the demand for the equipment based on market data and improve forecast accuracy. The forecasting unit can also refer to other external data to improve forecast accuracy. For example, the forecasting unit can refer to environmental data or economic data to help evaluate the condition of the equipment. In this way, forecast accuracy is improved by referring to relevant external data. Some or all of the above processing in the forecasting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the forecasting unit can input relevant external data into a generative AI and have the generative AI perform the forecast accuracy improvement.

[0082] The exchange unit can estimate the user's emotions and adjust the timing of the exchange process based on those emotions. For example, if the user is stressed, the exchange unit can delay the exchange process. For instance, it can use an emotion analysis algorithm to estimate the user's emotions and assess their stress level. The exchange unit can also speed up the exchange process if the user is relaxed. For example, it can use voice analysis or facial recognition technology to estimate the user's emotions and assess their level of relaxation. The exchange unit can also perform the exchange quickly if the user is in a hurry. For example, it can use text analysis technology to estimate the user's emotions and assess their degree of urgency. By adjusting the timing of the exchange process according to the user's emotions, work efficiency is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the exchange unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the exchange unit can input user emotion data into the generating AI and have the generating AI perform timing adjustments for the exchange operation.

[0083] The replacement unit can select the optimal replacement method at the time of replacement, taking into account the operating history and maintenance history of the equipment. For example, the replacement unit can select the optimal replacement method based on the operating history. For example, the replacement unit can select the optimal replacement method based on the operating time and operating status of the equipment. The replacement unit can also select the optimal replacement method based on the maintenance history. For example, the replacement unit can evaluate the condition of the equipment after maintenance based on the maintenance history and select the optimal replacement method. The replacement unit can also select the optimal replacement method by combining the operating history and maintenance history. For example, the replacement unit can integrate the operating history and maintenance history, comprehensively evaluate the condition of the equipment, and select the optimal replacement method. In this way, the optimal replacement method can be selected by taking into account the operating history and maintenance history of the equipment. Some or all of the above processing in the replacement unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the replacement unit can input the operating history and maintenance history data of the equipment into a generation AI and have the generation AI select the optimal replacement method.

[0084] The replacement unit can optimize replacement work according to the type and application of the equipment. For example, the replacement unit can optimize the replacement work of network equipment. For example, the replacement unit can select the optimal replacement procedure based on the characteristics of the network equipment, thereby improving the efficiency of the replacement work. The replacement unit can also optimize the replacement work of server equipment. For example, the replacement unit can select the optimal replacement procedure based on the characteristics of the server equipment, thereby improving the efficiency of the replacement work. The replacement unit can also optimize the replacement work of sensor equipment. For example, the replacement unit can select the optimal replacement procedure based on the characteristics of the sensor equipment, thereby improving the efficiency of the replacement work. As a result, work efficiency is improved by performing replacement work according to the type and application of the equipment. Some or all of the above processing in the replacement unit may be performed using, for example, a generation AI, or without a generation AI. For example, the replacement unit can input data according to the type and application of the equipment into the generation AI and have the generation AI perform the optimization of the replacement work.

[0085] The communication unit can estimate the user's emotions and prioritize communication tasks based on those emotions. For example, if the user is stressed, the communication unit will prioritize important communication tasks. For instance, it can use an emotion analysis algorithm to estimate the user's emotions and assess their stress level. Similarly, if the user is relaxed, the communication unit can prioritize detailed communication tasks. For example, it can use voice analysis or facial recognition technology to estimate the user's emotions and assess their level of relaxation. Furthermore, if the user is in a hurry, the communication unit can perform communication tasks quickly. For example, it can use text analysis technology to estimate the user's emotions and assess their degree of urgency. This allows the system to prioritize communication tasks according to the user's emotions, thereby prioritizing important tasks. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the exchange unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the exchange unit can input user emotion data into a generative AI and have the generative AI determine the priority of exchange tasks.

[0086] The replacement unit can classify replacement work by region, taking into account the geographical distribution of the equipment during replacement. For example, the replacement unit can prioritize the replacement of equipment in geographically important regions. For example, the replacement unit can acquire geographical location information of equipment in geographically important regions and prioritize the replacement of equipment in those regions. The replacement unit can also prioritize the replacement of equipment in disaster-stricken areas. For example, the replacement unit can acquire geographical location information of equipment in disaster-stricken areas and prioritize the replacement of equipment in those regions. The replacement unit can also prioritize the replacement of equipment in high-traffic areas. For example, the replacement unit can acquire geographical location information of equipment in high-traffic areas and prioritize the replacement of equipment in those regions. This allows for early detection of regional anomalies by considering the geographical distribution of the equipment. Some or all of the above processing in the replacement unit may be performed using, for example, a generation AI, or without a generation AI. For example, the replacement unit can input the geographical location information of the equipment into a generation AI and have the generation AI perform the regional classification of replacement work.

[0087] The replacement unit can improve the efficiency of replacement work by referring to relevant external data (e.g., weather data or market data) during replacement. For example, the replacement unit can refer to weather data to determine the optimal timing for replacement work. For example, the replacement unit can identify the optimal timing for replacement work based on weather data and improve work efficiency. The replacement unit can also refer to market data to check the inventory status of replacement parts. For example, the replacement unit can evaluate the inventory status of replacement parts based on market data and determine the optimal replacement timing. The replacement unit can also improve the efficiency of replacement work by referring to other external data. For example, the replacement unit can refer to environmental data or economic data to identify the optimal timing for replacement work. In this way, the efficiency of replacement work is improved by referring to relevant external data. Some or all of the above processing in the replacement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the replacement unit can input relevant external data into a generative AI and have the generative AI perform the task of improving the efficiency of replacement work. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, and replacement unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects syslog, trap, and cip level information from each device. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to evaluate the status of the equipment. The prediction unit is implemented by the identification processing unit 290 of the data processing unit 12 and predicts the occurrence of equipment failure based on the analysis results. The replacement unit is implemented by the control unit 46A of the smart device 14 and performs replacement in advance based on the predicted equipment failure. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, and replacement unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects syslog, trap, and cip level information from each device. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to evaluate the status of the equipment. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and predicts the occurrence of equipment failure based on the analysis results. The replacement unit is implemented, for example, by the control unit 46A of the smart glasses 214 and performs replacement in advance based on the predicted equipment failure. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, and replacement unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects syslog, trap, and cip level information from each device. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to evaluate the status of the equipment. The prediction unit is implemented by the identification processing unit 290 of the data processing unit 12 and predicts the occurrence of equipment failure based on the analysis results. The replacement unit is implemented by the control unit 46A of the headset terminal 314 and performs replacement in advance based on the predicted equipment failure. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, and replacement unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects syslog, trap, and cip level information from each device. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to evaluate the status of the equipment. The prediction unit is implemented by the identification processing unit 290 of the data processing unit 12 and predicts the occurrence of equipment failure based on the analysis results. The replacement unit is implemented by the control unit 46A of the robot 414 and performs replacement in advance based on the predicted equipment failure.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The data collection unit can optimize the timing of information collection based on the operating time and environment of each device. For example, if a device has a long operating time, the information collection timing can be set to be shorter to monitor its status in real time. For example, for a device with more than 1000 operating hours, the information collection frequency can be set to every hour. Also, if the operating environment is harsh, the information collection timing can be set to be more frequent to detect abnormalities early. For example, for a device operating in a high-temperature and high-humidity environment, the information collection frequency can be set to every 30 minutes. Furthermore, if the operating time is short, the information collection timing can be set to be longer to reduce the system load. For example, for a device with less than 100 operating hours, the information collection frequency can be set to every day. In this way, by optimizing the timing of information collection according to the operating time and environment of each device, abnormalities can be detected early.

[0090] The analysis unit can improve analysis accuracy by considering the device's operating history and maintenance history during the analysis process. For example, it can analyze the frequency of abnormalities by considering the device's operating history. For instance, it can evaluate the frequency of abnormalities based on the device's operating time and operating conditions. It can also analyze the state after maintenance by considering the maintenance history. For example, it can evaluate the state of the device after maintenance based on the maintenance history and predict the risk of abnormalities occurring. Furthermore, it can improve analysis accuracy by combining the operating history and maintenance history. For example, it can integrate the operating history and maintenance history to comprehensively evaluate the state of the device. In this way, considering the device's operating history and maintenance history improves analysis accuracy.

[0091] The prediction unit can improve prediction accuracy by considering the device's past failure data and maintenance history during the prediction process. For example, it can predict the probability of failure based on past failure data. For example, it can calculate the probability of failure based on past failure data and evaluate the risk of failure. It can also predict the probability of failure after maintenance based on maintenance history. For example, it can calculate the probability of failure after maintenance based on maintenance history and evaluate the risk of failure. Furthermore, it can improve prediction accuracy by combining past failure data and maintenance history. For example, it can integrate past failure data and maintenance history to comprehensively evaluate the probability of failure. In this way, prediction accuracy is improved by considering the device's past failure data and maintenance history.

[0092] The replacement unit can select the optimal replacement method by considering the device's operating history and maintenance history during replacement. For example, the optimal replacement method can be selected based on the operating history. For example, the optimal replacement method can be selected based on the device's operating time and operating status. It can also be selected based on the maintenance history. For example, the device's condition after maintenance can be evaluated based on the maintenance history, and the optimal replacement method can be selected. Furthermore, the optimal replacement method can be selected by combining the operating history and maintenance history. For example, the operating history and maintenance history can be integrated to comprehensively evaluate the device's condition and select the optimal replacement method. In this way, the optimal replacement method can be selected by considering the device's operating history and maintenance history.

[0093] The data collection unit can gather information from social media and other external data sources to help evaluate the condition of the device. For example, it can collect anomaly reports from social media and use them to evaluate the device's condition. For example, it can analyze social media posts to gather information about device anomalies. It can also collect environmental information from other external data sources and use it to evaluate the device's condition. For example, it can collect weather data and environmental sensor data and use it to evaluate the device's condition. It can also collect traffic information from external data sources and use it to evaluate the device's condition. For example, it can collect network traffic data and use it to evaluate the device's condition. As a result, the accuracy of the device's condition evaluation is improved by collecting information from external data sources.

[0094] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection frequency can be set lower to reduce the system load. For instance, an emotion analysis algorithm can be used to estimate the user's emotions and assess their stress level. Conversely, if the user is relaxed, the data collection frequency can be set higher to collect more detailed data. For example, voice analysis or facial recognition technology can be used to assess the user's level of relaxation. Furthermore, if the user is in a hurry, the data collection frequency can be optimized to quickly collect only the necessary data. For example, text analysis technology can be used to assess the user's degree of urgency. In this way, the system load can be optimized by adjusting the data collection frequency according to the user's emotions.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. For instance, it can use an emotion analysis algorithm to estimate the user's emotions and evaluate their level of tension. If the user is relaxed, it can also provide a display method that includes detailed information. For example, it can use voice analysis or facial recognition technology to evaluate the user's level of relaxation. If the user is in a hurry, it can also provide a display method that focuses on the essentials. For example, it can use text analysis technology to evaluate the user's degree of urgency. By adjusting the display method of the analysis results according to the user's emotions, visibility is improved.

[0096] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. For instance, it can use an emotion analysis algorithm to estimate the user's emotions and assess their level of tension. If the user is relaxed, it can also provide a display method that includes detailed information. For example, it can use voice analysis or facial recognition technology to assess the user's level of relaxation. Furthermore, if the user is in a hurry, it can provide a display method that focuses on the essentials. For example, it can use text analysis technology to assess the user's degree of urgency. By adjusting the display method of the prediction results according to the user's emotions, visibility is improved.

[0097] The exchange unit can estimate the user's emotions and adjust the timing of the exchange process based on those emotions. For example, if the user is stressed, the exchange process can be delayed. For instance, an emotion analysis algorithm can be used to estimate the user's emotions and assess their stress level. Conversely, if the user is relaxed, the exchange process can be sped up. For example, voice analysis or facial recognition technology can be used to assess the user's level of relaxation. Furthermore, if the user is in a hurry, the exchange process can be performed quickly. For example, text analysis technology can be used to assess the user's degree of urgency. By adjusting the timing of the exchange process according to the user's emotions, work efficiency is improved.

[0098] The exchange unit can estimate the user's emotions and prioritize exchange tasks based on those emotions. For example, if the user is stressed, important exchange tasks will be prioritized. For instance, an emotion analysis algorithm can be used to estimate the user's emotions and assess their stress level. If the user is relaxed, detailed exchange tasks can be prioritized. For example, voice analysis or facial recognition technology can be used to assess the user's level of relaxation. If the user is in a hurry, exchange tasks can be performed quickly. For example, text analysis technology can be used to assess the user's degree of urgency. By prioritizing exchange tasks according to the user's emotions, important tasks can be prioritized.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The collection unit collects syslog, trap, and CIP-level information from each device. For example, it collects information such as CPU usage, memory usage, and temperature for each device, and periodically collects syslog to obtain error logs and warning messages. It can also collect traps in real time to obtain status information. Furthermore, it can collect CIP-level information to understand the detailed status. Step 2: The analysis unit analyzes the information collected by the collection unit and evaluates the status of the equipment. For example, it can use AI, deep learning, or neural networks to analyze the collected information and detect anomalies. It can also execute an anomaly detection algorithm based on the collected information to determine whether the state is normal or abnormal. Step 3: The prediction unit predicts the occurrence of equipment failures based on the analysis results obtained by the analysis unit. For example, it uses machine learning models, regression models, or classification models that predict the occurrence of equipment failures based on past data to predict the risk and timing of failures. Step 4: The replacement unit performs replacements in advance based on equipment failures predicted by the prediction unit. For example, it prioritizes replacing equipment with a high predicted failure risk and plans replacement work based on the timing of failures. The replacement work plan may also be displayed through a web application or mobile application.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects syslog, trap, and cip level information from each device, An analysis unit analyzes the information collected by the aforementioned collection unit and evaluates the status of the equipment, A prediction unit predicts the occurrence of equipment failure based on the analysis results obtained by the aforementioned analysis unit, The system includes a replacement unit that performs replacements in advance based on equipment failures predicted by the prediction unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information such as CPU usage, memory usage, and temperature for each device. The system according to feature 1.

3. The aforementioned analysis unit, The system uses AI to analyze collected information and evaluate the status of the equipment. The system according to feature 1.

4. The prediction unit, We use machine learning models that predict equipment failures based on historical data. The system according to feature 1.

5. The aforementioned replacement part is The equipment is replaced in advance based on the equipment failure predicted by the prediction unit. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of information collection based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is Optimize the timing of information collection based on the operating time and usage environment of each device. The system according to feature 1.

8. The aforementioned collection unit is Customize the types of information to be collected according to the type and purpose of the device. The system according to feature 1.

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