中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

21 records (took 0.017 seconds)

    Machine-learning based prioritization of alert groupings
    11.
    发明授权
    Machine-learning based prioritization of alert groupings 有权

    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.

    Anomaly Detection Through Clustering of Time-Series Data Subsequences and Determination of Adaptive Thresholding
    15.
    发明申请
    Anomaly Detection Through Clustering of Time-Series Data Subsequences and Determination of Adaptive Thresholding 有权

    公开(公告)号：US20250028737A1

    公开(公告)日：2025-01-23

    申请号：US18222863

    申请日：2023-07-17

    Applicant: Splunk Inc.

    Inventor： Houwu Bai ,  Kristal Curtis ,  William Deaderick ,  Tanner Gilligan ,  Poonam Yadav ,  Om Rajyaguru

    IPC: G06F16/28 ,  G06F16/2458

    Abstract: Computerized methodologies are disclosed that are directed to detecting anomalies within a time-series data set. An aspect of the anomaly detection process includes determining one or more seasonality patterns that correspond to a specific time-series data set by evaluating a set of candidate seasonality patterns (e.g., hourly, daily, weekly, day-start off-sets, etc.). The evaluation of a candidate seasonality pattern may include dividing the time-series data set into a collection of subsequences based on the particular candidate seasonality pattern. Further, the collection of subsequences may be divided into clusters and a silhouette score may be computed to measure the clustering quality of the candidate seasonality pattern. In some instances, the candidate seasonality pattern having the highest silhouette score is selected and utilized in anomaly detection process. In other instances, a plurality of seasonality patterns may be combined forming a time policy, where the time policy is utilized in anomaly detection process.

    Hyperparameter tuning for anomaly detection service implementing machine learning forecasting
    16.
    发明授权
    Hyperparameter tuning for anomaly detection service implementing machine learning forecasting 有权

    公开(公告)号：US12158880B1

    公开(公告)日：2024-12-03

    申请号：US17978153

    申请日：2022-10-31

    Applicant: SPLUNK, INC.

    Inventor： Kristal Curtis ,  William Deaderick ,  Tanner Gilligan ,  Joseph Ross ,  Abraham Starosta ,  Sichen Zhong

    IPC: G06F16/22 ,  G06F16/242 ,  G06F16/2458 ,  G06F16/28

    Abstract: Implementations of this disclosure provide an anomaly detection system and methods of performing anomaly detection on a time-series dataset. The anomaly detection may include utilization of a forecasting machine learning algorithm to obtain a prediction of points of the dataset and comparing the predicted value of a point in the dataset with the actual value to determine an error value associated with that point. Additionally, the anomaly detection may include determination of a sensitivity threshold that impacts whether points within the dataset associated with certain error values are flagged as anomalies. The forecasting machine learning algorithm may implement a seasonality component determination process that accounts for seasonality or patterns in the dataset. A search query statement may be automatically generated through importing the sensitivity threshold into a predetermined search query statement that implements that forecasting machine learning algorithm.

    System and method for automated determination of search query parameters for anomaly detection
    17.
    发明授权
    System and method for automated determination of search query parameters for anomaly detection 有权

    公开(公告)号：US12008046B1

    公开(公告)日：2024-06-11

    申请号：US17837931

    申请日：2022-06-10

    Applicant: Splunk, Inc.

    Inventor： Kristal Curtis ,  William Deaderick ,  Abraham Starosta

    IPC: G06F16/903 ,  H04L41/069

    CPC classification number: G06F16/90335 ,  H04L41/069

    Abstract: A computerized method is disclosed that includes operations of obtaining a data set, selecting candidate parameter pairs to be analyzed, wherein the candidate parameter pairs include a window length and a sensitivity multiplier, and wherein the window length is a number of data points, performing an anomaly detection process for each candidate parameter pair including importing each candidate parameter pair into a predetermined search query thereby generating a set of populated predetermined search queries, wherein the predetermined search query is configured to perform the anomaly detection process, executing each search query of the set of populated predetermined search queries on the data set to obtain a set of anomaly detection results, and scoring each anomaly detection result by applying a set of heuristics to the set of the anomaly detection results, and generating an auto-tuned search query by selecting a first candidate parameter pair based on a score of each of the set of anomaly detection results and importing the first candidate parameter pair into the predetermined search query.

    Data stream generation based on sourcetypes associated with messages
    18.
    发明授权
    Data stream generation based on sourcetypes associated with messages 有权

    公开(公告)号：US11853303B1

    公开(公告)日：2023-12-26

    申请号：US17411357

    申请日：2021-08-25

    Applicant: SPLUNK Inc.

    Inventor： Adam Oliner ,  Eric Sammer ,  Kristal Curtis ,  Nghi Nguyen

    IPC: G06F16/00 ,  G06F16/2455 ,  G06F40/205 ,  G06F16/248 ,  G06N5/04

    CPC classification number: G06F16/24568 ,  G06F16/248 ,  G06F16/24564 ,  G06F40/205 ,  G06N5/04

    Abstract: As described herein, a portion of machine data of a message may be analyzed to infer, using an inference model, a sourcetype of the message. The portion of machine data may be generated by one or more components in an information technology environment. Based on the inference, a set of extraction rules associated with the sourcetype may be selected. Each extraction rule may define criteria for identifying a sub-portion of text from the portion of machine data of the message to produce a value. The set of extraction rules may be applied to the portion of machine data of the message to produce a result set that indicates a number of values identified using the set of extraction rules. Based on the result set, at least one action may be performed on one or more of inference data associated with the inference model and one or more messages.

    Feedback on inferred sourcetypes
    19.
    发明授权
    Feedback on inferred sourcetypes 有权

    公开(公告)号：US11748358B2

    公开(公告)日：2023-09-05

    申请号：US16175642

    申请日：2018-10-30

    Applicant: Splunk, Inc.

    Inventor： Adam Oliner ,  Eric Sammer ,  Kristal Curtis ,  Nghi Nguyen

    IPC: G06F16/245 ,  G06F16/2455 ,  G06F40/205 ,  G06F16/248 ,  G06N5/04

    CPC classification number: G06F16/24568 ,  G06F16/248 ,  G06F16/24564 ,  G06F40/205 ,  G06N5/04

    Abstract: As described herein, a portion of machine data of a message may be analyzed to infer, using an inference model, a sourcetype of the message. The portion of machine data may be generated by one or more components in an information technology environment. Based on the inference, a set of extraction rules associated with the sourcetype may be selected. Each extraction rule may define criteria for identifying a sub-portion of text from the portion of machine data of the message to produce a value. The set of extraction rules may be applied to the portion of machine data of the message to produce a result set that indicates a number of values identified using the set of extraction rules. Based on the result set, at least one action may be performed on one or more of inference data associated with the inference model and one or more messages.

Patent Agency Ranking