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US Patent 11687438 Adaptive thresholding of data streamed to a data processing pipeline

Patent 11687438 was granted and assigned to Splunk on June, 2023 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
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Patent attributes

Patent Applicant
Splunk
Splunk
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Current Assignee
Splunk
Splunk
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
116874380
Date of Patent
June 27, 2023
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Patent Application Number
172486120
Date Filed
January 29, 2021
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Patent Citations
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US Patent 9843596 Anomaly detection in dynamically evolving data and systems
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US Patent 10127258 Event time selection output techniques
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US Patent 10235638 Adaptive key performance indicator thresholds
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US Patent 10367827 Using network locations obtained from multiple threat lists to evaluate network data or machine data
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US Patent 10474680 Automatic entity definitions
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US Patent 10496817 Detecting anomalous values in small business entity data
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US Patent 10503348 Graphical user interface for static and adaptive thresholds
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US Patent 10536353 Control interface for dynamic substitution of service monitoring dashboard source data
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...
Patent Citations Received
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US Patent 12072769 Automatically halting cloud service deployments based on telemetry and alert data
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US Patent 11838351 Customizable load balancing in a user behavior analytics deployment
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Patent Primary Examiner
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John H Le
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CPC Code
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G06F 11/3466
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G06F 11/324
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G06F 11/3428
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G06F 11/3075
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Systems and methods are described for performing adaptive thresholding on key performance indicator (KPI) values using an online machine learning algorithm as the KPI values or the data from which the KPI values are derived is being ingested. For example, the system can identify outliers in a moving window of KPI values. To implement the adaptive thresholding, the system may identify seasonality and/or trend components in historical KPI values. When a new KPI value is obtained, the system may remove the identified seasonality and/or trend components from the KPI value, and determine whether the modified KPI value is an outlier using sketches or quantiles. The system can then repeat this process for each subsequently received KPI value.

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