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US Patent 11610441 Detecting and mitigating local individual driver anomalous behavior

Patent 11610441 was granted and assigned to State Farm on March, 2023 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
1

Patent attributes

Patent Applicant
State Farm
State Farm
1
Current Assignee
State Farm
State Farm
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
116104411
Patent Inventor Names
Michael Bernico1
Date of Patent
March 21, 2023
1
Patent Application Number
156298501
Date Filed
June 22, 2017
1
Patent Citations
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US Patent 10192369 Method and system for logging vehicle behaviour
1
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US Patent 10198879 Method and system for logging vehicle behaviour
1
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US Patent 10417837 Arrangements for collecting diagnostic information regarding vibrations of wheel-tire assembly and drive-line components of a wheeled vehicle
1
Patent Citations Received
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US Patent 11880750 Anomaly detection based on device vibration
2
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US Patent 11838189 Creating a budget-based time series
3
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US Patent 11685310 Warning condition adjusting apparatus and method
4
Patent Primary Examiner
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Jeffrey C Boomer
1
CPC Code
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G06Q 40/08
1
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G06N 20/00
1
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G07C 5/0816
1
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G07C 5/0841
1

Systems and methods for identifying anomalous driving behavior for a vehicle based on past driving behavior are disclosed herein. The method may include receiving a set of time-series driving data for the vehicle, wherein the set of time-series driving data is indicative of a set of operating conditions for the vehicle. Performing machine learning operations on the set of time-series driving data. Identifying a set of anomalous conditions in the time-series driving data based on a result set produced by the machine learning operations, wherein the set of anomalous conditions are indicative of an anomalous vehicle behavior. Comparing the set of anomalous conditions to a set of historical time-series driving data for the vehicle. Generating a vehicle feedback based on the time-series driving data and the comparison of the set of anomalous conditions to the set of historical time-series driving data.

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