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US Patent 10451712 Radar data collection and labeling for machine learning

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Patent
Patent
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Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
104517120
Patent Inventor Names
Upamanyu Madhow0
Babak Mamandipoor0
Hao Nan0
Mohammad Amin Arbabian0
Date of Patent
October 22, 2019
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Patent Application Number
162987910
Date Filed
March 11, 2019
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Patent Citations Received
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US Patent 12081646 Adaptively controlling groups of automated machines
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US Patent 10691133 Adaptive and interchangeable neural networks
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US Patent 10956807 Adaptive and interchangeable neural networks utilizing predicting information
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US Patent 11639985 Three-dimensional feature extraction from frequency modulated continuous wave radar signals
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US Patent 12092734 Partially-learned model for speed estimates in radar tracking
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US Patent 12078715 Radar tracking with model estimates augmented by radar detections
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US Patent 11774582 Imaging and radar fusion for multiple-object tracking
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US Patent 11774553 Parametric CNN for radar processing
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Patent Primary Examiner
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Frank J McGue
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Patent abstract

Systems and methods for labeling radar tracks for machine learning are disclosed. According to some aspects, a machine accesses data from radar unit(s), the data from the radar unit(s) comprising radar tracks, each radar track comprising one or more of the following: Doppler and micro-Doppler measurement(s), range measurement(s), and angle measurement(s). The machine accesses data from computer vision device(s), the data from the computer vision device(s) comprising image(s), the data from the computer vision device(s) being associated with a common geographic region and a common time period with the data from the radar unit(s). The machine labels, using an image recognition module, objects in the image(s). The machine determines, based on the common geographic region and the common time period, that labeled object(s) in the image(s) map to radar track(s). The machine labels the radar track(s) based on labels of the labeled objects.

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