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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.

Patent No.:

US 12694329 B1

PDFFull Text
Date of Patent:
Jul. 28, 2026

Filed:

Aug. 02, 2022

Generating high-resolution concentration maps for atmospheric gases using geography-informed machine learning

Applicant:

Palo Alto Research Center Incorporated, Palo Alto, CA (US);

Inventors:

Kalaivani Ramea Kubendran, Fremont, CA (US);

MD Nurul Huda, Hillsboro, OR (US);

David Schwartz, Concord, MA (US);

Jeyasri Subramanian, Sunnyvale, CA (US);

Assignee:

Genesee Valley Innovations, LLC, Santa Clara, CA (US);

Attorneys:

Fleit Intellectual Property Law

Jon Gibbons

Primary Examiner:

Haimei Jiang

Int. Cl.
CPC ...
G06N 20/00 (2019.01); G01S 13/90 (2006.01); G06F 16/29 (2019.01); G06F 18/214 (2023.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G01S 13/9027 (2019.05); G06F 16/29 (2019.01); G06F 18/214 (2023.01);
Abstract

A geography-informed machine learning (GIML) model is trained on a first remote sensing dataset corresponding to a first geographic area and including a first set of atmospheric gas concentration data for at least one atmospheric gas, a first set of multispectral data, and a first set of spatially autocorrelated land use classifications. The GIML model receives input including a second remote sensing dataset corresponding to a second geographic area. The second remote sensing dataset includes a second set of atmospheric gas concentration data for the atmospheric gas, a second set of multispectral data, and a second set of spatially autocorrelated land use classifications. The GIML model generates, for the second geographic area, a plurality of predicted atmospheric gas concentration values for the atmospheric gas having a spatial resolution that is greater than a spatial resolution of the first and second sets of atmospheric gas concentration data.

Find Patent Forward Citations

Ramea Kubendran, Kalaivani. (2026). Generating high-resolution concentration maps for atmospheric gases using geography-informed machine learning (U.S. Patent No. 12694329). U.S. Patent and Trademark Office. https://idiyas.com/patent/badge/12694329

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