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US Patent 11675816 Grouping evens into episodes using a streaming data processor

Patent 11675816 was granted and assigned to Splunk on June, 2023 by the United States Patent and Trademark Office.

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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
116758160
Date of Patent
June 13, 2023
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Patent Application Number
171632580
Date Filed
January 29, 2021
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Patent Citations
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US Patent 10127258 Event time selection output techniques
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US Patent 9639662 Systems and methods for event stream platforms which enable applications
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US Patent 7937344 Machine data web
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US Patent 8126891 Future data event prediction using a generative model
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US Patent 8751529 Scalable interactive display of distributed data
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US Patent 9215240 Investigative and dynamic detection of potential security-threat indicators from events in big data
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US Patent 9286413 Presenting a service-monitoring dashboard using key performance indicators derived from machine data
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Patent Citations Received
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US Patent 12093374 Cybersecurity incident response techniques utilizing artificial intelligence
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US Patent 11843528 Lower-tier application deployment for higher-tier system
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US Patent 11924026 System and method for alert analytics and recommendations
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US Patent 12001549 Cybersecurity incident response techniques utilizing artificial intelligence
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US Patent 12001550 Cybersecurity incident response techniques utilizing artificial intelligence
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Patent Primary Examiner
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Greta L. Robinson
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CPC Code
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G06F 16/285
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G06N 20/00
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Systems and methods are described for using a streaming data processor to group notable events reflecting operation of a computing system into episodes of related events reflecting an incident on the computing system, such as to enable root cause analysis of the incident. Each notable event can be generated based on one or more events detected within raw machine data. The streaming data processor can ingest a data stream of notable events, and apply a clustering algorithm to the events to cluster those events into episodes. When the episodes satisfy an action rule, the streaming data processor can take an action appropriate to that rule, such as transmitting an alert or programmatically altering operation of the computing system. The streaming data processor can utilize feedback as to the grouping of events into episodes to modify the clustering algorithm and improve accuracy of clustering.

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