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US Patent 10454753 Ranking network anomalies in an anomaly cluster

Patent 10454753 was granted and assigned to Lightbend, Inc. on October, 2019 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
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Lightbend, Inc.
Current Assignee
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Lightbend, Inc.
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10454753
Date of Patent
October 22, 2019
Patent Application Number
15494519
Date Filed
April 23, 2017
Patent Citations Received
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US Patent 12130878 Deduplication of monitored communications data in a cloud environment
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US Patent 12120140 Detecting threats against computing resources based on user behavior changes
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US Patent 12126643 Leveraging generative artificial intelligence (‘AI’) for securing a monitored deployment
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US Patent 12126695 Enhancing security of a cloud deployment based on learnings from other cloud deployments
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US Patent 11539749 Systems and methods for alert prioritization using security events graph
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US Patent 10805151 Method, apparatus, and storage medium for diagnosing failure based on a service monitoring indicator of a server by clustering servers with similar degrees of abnormal fluctuation
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US Patent 10860939 Application performance analyzer and corresponding method
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US Patent 11256759 Hierarchical graph analysis
...
Patent Primary Examiner
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Christopher S McCarthy
Patent abstract

The technology disclosed relates to organizing network performance metrics into historical anomaly dependency data. In particular, it relates to calculating cascading failure relationships between correlated anomalies detected in a network. It also relates to illustrating to a network administrator causes of system failure by laying out the graph to show a progression over time of the cascading failures and identify root causes of the cascading failures. It also relates to ranking anomalies and anomaly clusters in the network based on attributes of the resources exhibiting anomalous performances and attributes of the anomalous performances. It further relates to depicting evolution of resource failures across a network by visually coding impacted resources and adjusting the visual coding over time and allowing replay over time to visualize propagation of anomalous performances among the impacted resource.

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