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US Patent 10261777 Detecting anomalies online using histograms of ECU processing activity

Patent 10261777 was granted and assigned to Aurora Labs on April, 2019 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Aurora Labs
Aurora Labs
Current Assignee
Aurora Labs
Aurora Labs
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10261777
Patent Inventor Names
Zohar Fox0
Date of Patent
April 16, 2019
Patent Application Number
16044369
Date Filed
July 24, 2018
Patent Citations
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US Patent 10042635 Method for wireless remote updating vehicle software
0
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US Patent 10020995 Vehicle middleware
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US Patent 10108191 Driver interactive system for semi-autonomous modes of a vehicle
Patent Citations Received
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US Patent 11422792 Management of transport software updates
0
0
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US Patent 11868764 Management of transport software updates
0
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US Patent 10649839 Detecting anomalies using real-time controller processing activity
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US Patent 10866802 Detecting anomalies using real-time controller processing activity
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US Patent 11271971 Device for facilitating managing cyber security health of a connected and autonomous vehicle (CAV)
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US Patent 11294662 Management of transport software updates
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US Patent 11334346 Detecting anomalies using real-time controller processing activity
...
Patent Primary Examiner
‌
Thuy Dao
Patent abstract

Disclosed embodiments relate to identifying Electronic Control Unit (ECU) anomalies in a vehicle. Operations may include monitoring, in the vehicle, data representing real-time processing activity of the ECU; accessing, in the vehicle, historical data relating to processing activity of the ECU, the historical data representing expected processing activity of the ECU; comparing, in the vehicle, the real-time processing activity data with the historical data, to identify at least one anomaly in the real-time processing activity of the ECU; and implementing a control action for the ECU when the at least one anomaly is identified.

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