公开(公告)号：US12181956B1

公开(公告)日：2024-12-31

申请号：US18208879

申请日：2023-06-12

Applicant: Splunk Inc.

Inventor： Kristal Curtis ,  William Deaderick ,  Wei J. Gao ,  Tanner Gilligan ,  Chandrima Sarkar ,  Aleksander Stojanovic ,  Ralph Donald Thompson ,  Poonam Yadav ,  Sichen Zhong

IPC: G06F11/30 ,  G06F11/07 ,  G06F18/21 ,  G06F18/214

Abstract: Systems and methods are disclosed that are directed to improving the prioritization, display, and viewing of system alerts through the use of machine learning techniques to group the alerts and further to prioritize the groupings. Additionally, a graphical user interface is generated that illustrates the prioritized listing of the plurality of groupings. Thus, a system administrator or other user receives an improved experience as the number of notifications provided to the system administrator are reduced due to the grouping of individual alerts into related groupings and further due to the prioritization of the groupings. Previously, or in current technology, system alerts may be automatically generated and provided immediately to a system administrator. In some instances, any advantage of detecting system errors or system monitoring provided by the alerts is negated by the vast number of alerts and provision of minimally important alerts in a manner that concealed more important alerts.

公开(公告)号：US11714698B1

公开(公告)日：2023-08-01

申请号：US17587877

申请日：2022-01-28

Applicant: Splunk, Inc.

Inventor： Kristal Curtis ,  William Deaderick ,  Wei Jie Gao ,  Tanner Gilligan ,  Chandrima Sarkar ,  Alexander Stojanovic ,  Ralph Donald Thompson ,  Sichen Zhong ,  Poonam Yadav

IPC: G06F11/30 ,  G06F11/07 ,  G06F18/214 ,  G06F18/21

CPC classification number: G06F11/0781 ,  G06F11/0769 ,  G06F18/214 ,  G06F18/2178

Abstract: A computerized method is disclosed for generating a prioritized listing of alerts based on scoring by a machine learning model and retraining the model based on user feedback. Operations of the method include receiving a plurality of alerts, generating a score for each of the plurality of alerts through evaluation of each of the plurality of alerts by a machine learning model, generating a prioritized listing of the plurality of alerts based on the generated scores, receiving user feedback on the prioritized listing, retraining the machine learning model based on the user feedback by generating a set of labeled alert pairs, wherein a labeled alert pair includes a first alert, a second alert, and an indication as to which of the first alert or the second alert is a higher priority in accordance with the user feedback, and evaluating subsequently received alerts with the retrained machine learning model.