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US Patent 10186123 Complex event recognition in a sensor network

Patent 10186123 was granted and assigned to Avigilon on January, 2019 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Avigilon
Avigilon
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Current Assignee
Avigilon
Avigilon
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
101861230
Patent Inventor Names
Hongli Deng0
Tae Eun Choe0
Atul Kanaujia0
Date of Patent
January 22, 2019
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Patent Application Number
146748890
Date Filed
March 31, 2015
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Patent Citations Received
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US Patent 12012118 Generating training datasets for training machine learning based models for predicting behavior of traffic entities for navigating autonomous vehicles
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US Patent 11069926 Controlling ongoing battery system usage via parametric linear approximation
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US Patent 11753046 System and method of predicting human interaction with vehicles
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US Patent 11763163 Filtering user responses for generating training data for machine learning based models for navigation of autonomous vehicles
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US Patent 11981352 System and method of predicting human interaction with vehicles
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US Patent 11987272 System and method of predicting human interaction with vehicles
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US Patent 11993291 Neural networks for navigation of autonomous vehicles based upon predicted human intents
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US Patent 11518413 Navigation of autonomous vehicles using turn aware machine learning based models for prediction of behavior of a traffic entity
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...
Patent Primary Examiner
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Vincent Rudolph
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Patent abstract

Systems, methods, and manufactures for a surveillance system are provided. The surveillance system includes sensors having at least one non-overlapping field of view. The surveillance system is operable to track a target in an environment using the sensors. The surveillance system is also operable to extract information from images of the target provided by the sensors. The surveillance system is further operable to determine probabilistic confidences corresponding to the information extracted from images of the target. The confidences include at least one confidence corresponding to at least one primitive event. Additionally, the surveillance system is operable to determine grounded formulae by instantiating predefined rules using the confidences. Further, the surveillance system is operable to infer a complex event corresponding to the target using the grounded formulae. Moreover, the surveillance system is operable to provide an output describing the complex event.

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