Patent ID: 11921573
Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for improving system uptime, the operations comprising:
monitoring computing resources of a system, the computing resources comprising hardware resources, communication resources, software resources, or a combination thereof;
obtaining, by the processor, metrics corresponding to a plurality of metrics associated with a system based on the monitoring, wherein the plurality of metrics comprise metrics corresponding to the computing resources of the system and include at least metrics selected from the group consisting of: a lock metric, a transaction log metric, a background processes metric, a dialog processes metric, an update processes metric, an update response time metric, or a combination thereof;
compiling a plurality of datasets based on the monitoring, wherein the plurality of datasets includes at least a first dataset and a second dataset, wherein the first dataset comprises first values for a first set of metrics obtained via the monitoring during a first period of time and the second dataset comprises second values for the first set of metrics obtained via the monitoring during a second period of time, wherein the first period of time and the second period of time are consecutive periods of time;
cleaning information included in the plurality of datasets, wherein the cleaning is configured to address errors in the plurality of datasets;
evaluating the first dataset and the second dataset using a machine learning model configured to:
determine a trend associated with the first set of metrics based on the first values and the second values, wherein the trend corresponds to a trend determined based on changes in the lock metric and the transaction log metric, changes in the background processes metric and the dialog processes metric, changes in the update processes metric and the lock metric, changes in the update processes metric and the transaction log metric, changes in the update processes metric and the update response time metric, or a combination thereof; and
predict a likelihood of a future system failure based on one or more of the trend determined based on the first values and the second values, wherein the machine learning model subsequently determines a second trend associated with the first set of metrics based on the second values of the second data and a third dataset of the plurality of datasets using the machine learning model, wherein the third dataset comprises third values for the first set of metrics obtained via the monitoring during a third period of time, wherein the second period of time and the third period of time are consecutive periods of time, and wherein the first dataset is omitted from the subsequent determination using the machine learning model;
generating a system classification output via a machine learning engine, the system classification output comprising information representative of whether a future system failure is predicted based on the one or more trends identified by the machine learning engine using the machine learning model; and
performing one or more actions to mitigate a system failure predicted by the system classification output.