US 12,254,403 B2
Method and system for fill level determination
Justin Armstrong, San Francisco, CA (US); Shandy Brown, San Francisco, CA (US); Mark Stefanski, San Francisco, CA (US); and Matthew Duncan, San Francisco, CA (US)
Assigned to Compology LLC, Pittsburgh, PA (US)
Filed by Compology LLC, Pittsburgh, PA (US)
Filed on Jan. 28, 2021, as Appl. No. 17/161,437.
Application 17/161,437 is a continuation in part of application No. 16/709,127, filed on Dec. 10, 2019, granted, now 10,943,356.
Claims priority of provisional application 62/778,775, filed on Dec. 12, 2018.
Prior Publication US 2021/0158097 A1, May 27, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/08 (2023.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/241 (2023.01); G06T 7/00 (2017.01); G06V 10/764 (2022.01); G06V 20/52 (2022.01); G06V 20/64 (2022.01)
CPC G06N 3/08 (2013.01) [G06F 18/2148 (2023.01); G06F 18/2193 (2023.01); G06F 18/241 (2023.01); G06T 7/001 (2013.01); G06V 10/764 (2022.01); G06V 20/52 (2022.01); G06V 20/64 (2022.01); G06T 2207/20084 (2013.01); G06T 2207/30164 (2013.01); G06T 2207/30232 (2013.01)] 12 Claims
OG exemplary drawing
 
1. A method for fullness metric assessment, comprising:
training a fullness metric classifier configured to classify fullness of container interiors by providing the classifier with a training set comprising a plurality of images of different container types in different states of fill;
receiving, from a content sensor, a subject image depicting a subject container interior of a subject container; and
using the fullness metric classifier, determining a fullness metric associated with the subject image, comprising:
providing the subject image and a reference image to the fullness metric classifier as an input, wherein the reference image depicts the subject container interior in the target fullness state of waste; and
in response to providing the subject image and the reference image to the fullness metric classifier, receiving, from the fullness metric classifier, information indicative of the fullness metric, wherein the information indicative of the fullness metric is determined based on the training set.