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Deep machine learning to predict and prevent adverse conditions at structural assets

  • US 10,664,750 B2
  • Filed: 08/10/2016
  • Issued: 05/26/2020
  • Est. Priority Date: 08/10/2016
  • Status: Active Grant
First Claim
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1. A computing system to predict and prevent instances of vegetative overgrowth on utility infrastructure, the computing system comprising:

  • at least one processor; and

    at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed by the at least one processor, cause the computing system to;

    obtain data descriptive of a plurality of images that depict at least a portion of a geographic area that contains a first utility asset, the plurality of images comprising at least a first image and a second image, the first image and the second image being different from each other in at least one of the following attributes;

    time of capture, location of capture, angle of capture, data type, and resolution;

    input data descriptive of at least the first image and the second image into a machine-learned vegetation overgrowth prediction model that comprises a machine-learned object detection model and a machine-learned vegetation overgrowth neural network;

    receive, as an output of the machine-learned object detection model, one or more object feature vectors that respectively describe one or more attributes of one or more objects included in the geographic area;

    input the one or more object feature vectors and at least one of the plurality of images into the machine-learned vegetation overgrowth neural network; and

    receive, as an output of the machine-learned vegetation overgrowth neural network, at least one prediction regarding the occurrence of a vegetative overgrowth condition at the first utility asset during one or more future time periods, the at least one prediction produced by the machine-learned vegetation overgrowth neural network based at least in part on the one or more object feature vectors and the at least one of the plurality of images.

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