The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 20, 2022

Filed:

Dec. 02, 2019
Applicant:

Here Global B.v., Eindhoven, NL;

Inventors:

Richard Kwant, Oakland, CA (US);

Anish Mittal, Berkeley, CA (US);

David Lawlor, Chicago, IL (US);

Zhanwei Chen, Oakland, CA (US);

Himaanshu Gupta, San Francisco, CA (US);

Assignee:

HERE Global B.V., Eindhoven, NL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 5/02 (2006.01); G06N 3/08 (2006.01); G06T 7/70 (2017.01); G06T 7/11 (2017.01); G06K 9/62 (2022.01); G06T 7/73 (2017.01); G06T 7/77 (2017.01); G06V 30/194 (2022.01);
U.S. Cl.
CPC ...
G06N 5/022 (2013.01); G06K 9/6218 (2013.01); G06N 3/08 (2013.01); G06T 7/11 (2017.01); G06T 7/70 (2017.01); G06T 7/73 (2017.01); G06T 7/77 (2017.01); G06V 30/194 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/10048 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30248 (2013.01); G06T 2207/30256 (2013.01); G06T 2207/30261 (2013.01);
Abstract

An approach is provided for a redundant feature detection engine. The approach, for instance, involves segmenting an input image into a plurality of grid cells for processing by the redundant feature detection engine. The redundant feature detection engine includes a neural network. The approach also involves, for each of the plurality of grid cells, initiating a prediction of an object code by the redundant feature detection engine. The object code is a predicted feature that uniquely identifies an object depicted in the input image. The approach further involves aggregating the plurality of grid cells into one or more clusters based on the object code predicted for said each grid cell. The approach further involves predicting one or more features of the object corresponding to a respective cluster of the one or more clusters by merging one or more feature prediction outputs of said each grid cell in the respective cluster.


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