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:
Jul. 30, 2024

Filed:

Nov. 15, 2021
Applicant:

Cape Analytics, Inc., Mountain View, CA (US);

Inventors:

Ingo Kossyk, Seefeld-Hechendorf, DE;

Suat Gedikli, Planegg, DE;

Assignee:

Cape Analytics, Inc., Palo Alto, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06F 18/21 (2023.01); G06F 18/22 (2023.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06T 7/73 (2017.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06V 20/17 (2022.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 18/217 (2023.01); G06F 18/22 (2023.01); G06N 3/04 (2013.01); G06T 7/74 (2017.01); G06T 7/97 (2017.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/17 (2022.01); G06V 20/176 (2022.01); G06V 20/188 (2022.01); G06T 2207/20084 (2013.01);
Abstract

Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.


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