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:
Aug. 26, 2025

Filed:

Aug. 19, 2022
Applicant:

X Development Llc, Mountain View, CA (US);

Inventors:

Gearoid Murphy, Mountain View, CA (US);

Alexander Holiday, Brookfield, WI (US);

Diosdado Banatao, Los Altos Hills, CA (US);

Allen Zhao, Mountain View, CA (US);

Shruti Badhwar, San Mateo, CA (US);

Assignee:

X Development LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/58 (2022.01); G01J 3/28 (2006.01); G01J 3/40 (2006.01); G06V 10/56 (2022.01); G06V 10/774 (2022.01); G06V 10/80 (2022.01); G06V 20/64 (2022.01);
U.S. Cl.
CPC ...
G06V 10/58 (2022.01); G01J 3/2823 (2013.01); G01J 3/40 (2013.01); G06V 10/56 (2022.01); G06V 10/774 (2022.01); G06V 10/806 (2022.01); G06V 20/64 (2022.01); G01J 2003/2826 (2013.01);
Abstract

Methods and systems for using multiple hyperspectral cameras sensitive to different wavelengths to predict characteristics of objects for further processing, including recycling, are described. The multiple hyperspectral images can be used to predict higher resolution spectra by using a trained machine learning model. The higher resolution spectra may be more easily analyzed to sort plastics into a recyclability category. The hyperspectral images may also be used to identify and analyze dark or black plastics, which are challenging for SWIR, MWIR, and other wavelengths. The machine learning model may also predict the base polymers and contaminants of plastic objects for recycling. The hyperspectral images may be used to predict recyclability and other characteristics using a trained machine learning model.


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