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US Patent 12118077 Feature extraction and time series anomaly detection over dynamic graphs

Patent 12118077 was granted and assigned to Intuit on October, 2024 by the United States Patent and Trademark Office.

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Contents

Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Applicant
Intuit
Intuit
1
Current Assignee
Intuit
Intuit
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
121180771
Patent Inventor Names
Aviv Ben Arie1
Or Basson1
Liat Ben Porat Roda1
Hagai Fine1
Miriam Hanna Manevitz1
Date of Patent
October 15, 2024
1
Patent Application Number
171542931
Date Filed
January 21, 2021
1
Patent Citations
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US Patent 9202052 Dynamic graph anomaly detection framework and scalable system architecture
1
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US Patent 8270614 Method of updating group key and group key update device using the same
1
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US Patent 8462161 System and method for fast component enumeration in graphs with implicit edges
1
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US Patent 8762298 Machine learning based botnet detection using real-time connectivity graph based traffic features
1
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US Patent 8909677 Providing a distributed balanced tree across plural servers
1
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US Patent 9015716 Proactive monitoring tree with node pinning for concurrent node comparisons
1
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US Patent 10826685 Combined blockchain integrity
1
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US Patent 10891377 Malicious software identification
1
...
Patent Primary Examiner
‌
David Garcia Cervetti
1
CPC Code
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G06N 20/00
1
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G06N 3/02
1
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G06F 2221/2101
1
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G06F 21/552
1
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G06F 2221/034
1
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H04L 63/1425
1
Patent abstract

A plurality of graph snapshots for a plurality of consecutive periodic time samples maps between connected components in consecutive graph snapshots and describes at least one feature of each connected component. A recursively-built tree tracks an evolution of one of the connected components through the plurality of graph snapshots, the tree including a root node representing the connected component at a final one of the consecutive periodic time samples and a plurality of leaf nodes branching from the root node. A plurality of paths is extracted from the tree by traversing the tree from the root node to respective ones of the plurality of leaf nodes. Each path contains data describing an evolution of a respective one of the connected components through time as indicated by evolution of the at least one feature of the respective one of the connected components. Each of the plurality of paths is converted into a respective numerical vector of a plurality of numerical vectors that may be used as inputs to a time series anomaly detection algorithm.

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