Patent Issued for Systems and methods for computer infrastructure monitoring and maintenance (USPTO 11868203): United Services Automobile Association
2024 JAN 25 (NewsRx) -- By a
The patent’s inventors are Carranza, Manuel A. (
This patent was filed on
From the background information supplied by the inventors, news correspondents obtained the following quote: “While it is common practice for businesses to monitor their computer networks and software processes, the amount of information that can be obtained may make it prohibitively expensive to diligently monitor that information. Thus, monitoring computer infrastructure may be a constant balancing act between gathering and analyzing enough information so as to reduce the chances of a problem escaping detection, while at the same time triaging the information gathered and analyzed so as to enable the monitoring process a realistic chance to perform the necessary and requested analysis.
“When a computer network or software process experiences a complete or partial failure, the resulting damage can be catastrophic. Many of today’s monitoring systems can only determine once a system is down, although there may have been certain events leading up to the system failure that could have indicated that the system was going to fail.
“Despite many attempts to address these problems, there remains a need for a solution to this problem that is both robust enough to dependably monitor important networks and help determine if a system failure may soon occur, and yet flexible enough to work with any computer or network infrastructure.
“Accordingly, there remains an unmet need for a sufficiently flexible and dependable approach to monitoring computer networks and software process output.”
Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “One or more examples described herein relate to monitoring computer infrastructures (e.g., a network, an individual computer system, software processes, or combinations thereof) to detect existing or developing computer issues. In one example, the analytics module compares performance from a given time frame (e.g., the present time or the very recent past) to another time frame (e.g., a week ago). This comparison may reveal one or more differences in terms of error messages, network traffic, computer resource utilization, software process performance, or another computer metric, and, based on the type or level of the difference, a notification (e.g., warning) may be generated and communicated to a network administrator.
“In an aspect, this disclosure is directed to a method. The method may include identifying a target computer system. A first set of data for a first time period relating an operating metric from the target computer system may be received. The operating metric may be stored. A second set of data for a second time period relating to the operating metric may be received. The first and second sets of data may be compared. A difference between the two sets of data may be identified. If the difference between the two sets of data is outside a predetermined range a determination may be made that there is an issue on the target computing system that needs to be resolved. A warning notification may be displayed in a graphical user interface, wherein the warning notification is displayed differently depending on a severity level of the warning. Instructions may be provided on how to resolve the issue.
“In another aspect, this disclosure is directed to a system. The system may include a computer device connected to a network and a memory electronically coupled to the computer device. The memory may include instructions that cause the at least one computer device to effectuate certain operations. The operations may include identifying a target computer system, receiving a first set of data relating an operating metric from the target computer system, storing the operating metric, receiving a second set of data relating to the operating metric, comparing the first and second sets of data, identifying a difference between the two sets of data, determining that there is an issue that needs to be resolved on the target computing system if the difference between the two sets of data is outside a predetermined range, displaying a warning notification in a graphical user interface, wherein the warning notification may be displayed differently depending on a severity level of the warning, and providing instructions on how to resolve the issue.
“According to another aspect, this disclosure is directed to a non-transitory computer readable storage medium. The non-transitory computer readable storage medium may include computer-executable instructions that cause a processor to effectuate certain operations. The operations may include identifying a target computer system, receiving a first set of data relating an operating metric from the target computer system, storing the operating metric, receiving a second set of data relating to the operating metric, comparing the first and second sets of data, identifying a difference between the two sets of data, determining that there is an issue that needs to be resolved on the target computing system if the difference between the two sets of data is outside a predetermined range, displaying a warning notification in a graphical user interface, wherein the warning notification may be displayed differently depending on a severity level of the warning, and providing instructions on how to resolve the issue.
“A component or a feature that is common to more than one drawing is indicated with the same reference number in each of the drawings.”
The claims supplied by the inventors are:
“1. A method comprising: filtering secondary data from error log data from a set of data, wherein the error log data relates to an operating metric from a target computer system; determining, based on receiving a user input indicating to calculate a number of errors of an error type and based on the filtered error log data, the number of errors of the error type; determining, based on the number of errors, a severity level of a warning associated with operation of the target computer system; outputting, based on the severity level of the warning, an indication of an issue associated with the operation of the target computer system; and requiring, based on the severity level, a response to the indication.
“2. The method of claim 1, wherein the operating metric is associated with at least one of CPU usage, GPU usage, memory usage, hard drive usage, software process performance metrics, network latency, network bandwidth, or error log messages.
“3. The method of claim 1, wherein the secondary data comprises account information.
“4. The method of claim 1, wherein the filtering the secondary data from the error log data comprises replacing the second secondary data with a generic filler.
“5. The method of claim 1, wherein the target computer system comprises a cloud-computing system.
“6. The method of claim 1, wherein the severity level is further based on an error type and the error type comprise errors associate with at least one of CPU usage, GPU usage, memory storage, hard drive usage, software process performance metrics, network latency, or network bandwidth.
“7. The method of claim 1, wherein the determining, based on the number of errors, the severity level of the warning associated with operation of the target computer system is further based on a change in the number of errors over a period of time.
“8. The method of claim 1, further comprising filtering the secondary data, wherein the secondary data comprises a string; and wherein the filtering the secondary data comprises removing a portion of the string that prevents aggregation of error messages and leaving a portion of the string that does not prevent aggregation of error messages.
“9. A computing device comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, causes the computing device to: filter secondary data from error log data from a set of data, wherein the error log data relates to an operating metric from a target computer system; determine based on receiving a user input indicating to calculate a number of errors of an error type and based on the filtered error log data, the number of errors of the error type; determine, based on the number of errors, a severity level of a warning associated with operation of the target computer system; output, based on the severity level of the warning, an indication of an issue associated with the operation of the target computer system; and require, based on the severity level, a response to the indication.
“10. The computing device of claim 9, wherein the operating metric comprises at least one of CPU usage, GPU usage, memory usage, hard drive usage, software process performance metrics, network latency, network bandwidth, or error log messages.
“11. The computing device of claim 9, wherein the instructions cause the computing device to filter the secondary data by replacing the secondary data with a generic filler.
“12. The computing device of claim 9, wherein the secondary data comprises a timestamp.
“13. The computing device of claim 12, wherein the secondary data comprises a string.
“14. The computing device of claim 13, wherein the instructions cause the computing device to filter the secondary data by truncating the string.
“15. A non-transitory computer-readable medium storing instructions that, when executed, cause operations comprising: filtering secondary data from error log data from a set of data, wherein the error log data relates to an operating metric from a target computer system; determining, based on receiving a user input indicating to calculate a number of errors of an error type and based on the filtered error log data, the number of errors of the error type; determining, based on the number of errors, a severity level of a warning associated with operation of the target computer system; and outputting, based on the severity level of the warning, an indication of an issue associated with the operation of the target computer system; and requiring, based on the severity level, a response to the indication.
“16. The non-transitory computer-readable medium of claim 15, wherein the operating metric comprises at least one of CPU usage, GPU usage, memory usage, hard drive usage, software process performance metrics, network latency, network bandwidth, or error log messages.
“17. The non-transitory computer-readable medium of claim 15, wherein the secondary data comprises at least one of a string, a generic filler, or data that prevents aggregation of error messages.
“18. The non-transitory computer-readable medium of claim 15, wherein the filtering the secondary data from the error log data comprises altering a timestamp to group error logs within a certain timeframe.
“19. The non-transitory computer-readable medium of claim 15, wherein the operations further comprise comparing the number of errors to a number of errors from another error log from another set of data; and wherein the determining the severity level is further based on comparing the number of errors.”
For the URL and additional information on this patent, see: Carranza, Manuel A. Systems and methods for computer infrastructure monitoring and maintenance.
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