IP Library › Granted Patent US 12,373,849
Granted Patent B2
US 12,373,849 · App. 17/935,399 · Granted Jul 29, 2025

Systems and methods for validating planting of trees

Inventor: Kenneth Jason Sanchez (San Francisco, CA)
Assignee: QUANATA, LLC
G06Q30/018B60W40/09G01C21/3469G01C21/3617G05D21/00G06E3/001G06F3/0482G06F3/0484G06G7/70G06Q30/0201G06Q30/0208G06Q30/0284G06Q30/0641G06Q40/08G06Q50/40G06T7/0002G06T11/00G06V20/188G07C5/0841G16Z99/00B60W2540/30
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Quick Facts
Patent No.
US 12,373,849
App. No.
17/935,399
Granted
Jul 29, 2025
Kind
B2
Abstract

Method and system for validating planting of trees. For example, the method includes receiving a first image depicting a tree, receiving a second image depicting an environment where the tree is to be planted, receiving a third image depicting the tree having been planted in the environment, selecting and encoding a first patch of the first image that depicts the tree, selecting and encoding a second patch of the second image that depicts the environment, selecting and encoding a third patch of the third image that depicts both the tree and the environment, and comparing the encoded patches to determine whether the tree has been planted in the environment.

Claims (111)

1. A computer-implemented method comprising:

selecting, by a computing device, a first patch of a first image that depicts a part of a tree or a second patch of a second image that depicts a part of an environment, and a third patch of a third image that depicts the part of the tree and the part of the environment;

encoding, by the computing device, the first patch of the first image to generate first encoded tree data or the second patch of the second image to generate first encoded environment data, and the third patch of the third image to generate third encoded tree data or third encoded environment data, wherein the encoding the first patch or the second patch involves learning to generate the first encoded tree data or the first encoded environment data, respectively, based upon a model, wherein the model is an artificial neural network;

analyzing, by the computing device, the first patch or the second patch, wherein the analyzing the first patch or the second patch includes performing one or more of feature extraction or applying pattern recognition;

using, by the computing device, an autoencoder to generate the first encoded tree data or the first encoded environment data, wherein the autoencoder includes a reduction layer for inputs, one or more intermediate layers, and a reconstruction layer for outputs;

comparing, by the computing device, the first encoded tree data with the third encoded tree data or the first encoded environment data with the third encoded environment data to generate one or more comparison data; and

determining, by the computing device, whether the tree is in the environment based at least in part upon the one or more comparison data.

2. The computer-implemented method of claim 1 , wherein the determining, by the computing device, whether the tree is in the environment based at least in part upon the one or more comparison data includes:

validating, by the computing device, that the tree is in the environment in response to the one or more comparison data satisfying one or more predetermined conditions.

3. The computer-implemented method of claim 1 , further comprising:

receiving, by the computing device, the first image depicting the tree;

selecting, by the computing device, the first patch of the first image that depicts the part of the tree;

encoding, by the computing device, the first patch of the first image to generate the first encoded tree data;

encoding, by the computing device, the third patch of the third image to generate the third encoded tree data; and

comparing, by the computing device, the first encoded tree data with the third encoded tree data to generate the one or more comparison data.

4. The computer-implemented method of claim 1 , further comprising:

receiving, by the computing device, the second image depicting the environment;

selecting, by the computing device, the second patch of the second image that depicts the part of the environment;

encoding, by the computing device, the second patch of the second image to generate the first encoded environment data;

encoding, by the computing device, the third patch of the third image to generate the third encoded environment data; and

comparing, by the computing device, the first encoded environment data with the third encoded environment data to generate the one or more comparison data.

5. The computer-implemented method of claim 1 , further comprising:

receiving, by the computing device, the first image depicting the tree;

receiving, by the computing device, the second image depicting the environment;

selecting, by the computing device, the first patch of the first image that depicts the part of the tree;

selecting, by the computing device, the second patch of the second image that depicts the part of the environment;

encoding, by the computing device, the first patch of the first image to generate the first encoded tree data;

encoding, by the computing device, the third patch of the third image to generate the third encoded tree data;

comparing, by the computing device, the first encoded tree data with the third encoded tree data to generate one or more comparison tree data;

encoding, by the computing device, the second patch of the second image to generate the first encoded environment data;

encoding, by the computing device, the third patch of the third image to generate the third encoded environment data;

comparing, by the computing device, the first encoded environment data with the third encoded environment data to generate one or more comparison environment data; and

determining, by the computing device, whether the tree is in the environment based at least in part upon the one or more comparison tree data and the one or more comparison environment data.

6. The computer-implemented method of claim 5 , wherein the determining, by the computing device, whether the tree is in the environment based at least in part upon the one or more comparison tree data and the one or more comparison environment data includes:

validating, by the computing device, that the tree is in the environment in response to the one or more comparison tree data satisfying one or more predetermined tree conditions and the one or more comparison environment data satisfying one or more predetermined environment conditions.

7. The computer-implemented method of claim 1 , further comprising:

capturing, by the computing device, one or more images associated with whether the tree is in the environment.

8. A computing device comprising:

one or more processors; and

a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:

select a first patch of a first image that depicts a part of a tree or a second patch of a second image that depicts a part of an environment, and a third patch of a third image that depicts the part of the tree and the part of the environment;

encode the first patch of the first image to generate first encoded tree data or the second patch of the second image to generate first encoded environment data, and the third patch of the third image to generate third encoded tree data or third encoded environment data, wherein encoding the first patch or the second patch involves learning to generate the first encoded tree data or the first encoded environment data, respectively, based upon a model, wherein the model is an artificial neural network;

analyze the first patch or the second patch, wherein analyzing the first patch or the second patch includes performing one or more of feature extraction or applying pattern recognition;

use an autoencoder to generate the first encoded tree data or the first encoded environment data, wherein the autoencoder includes a reduction layer for inputs, one or more intermediate layers, and a reconstruction layer for outputs;

compare the first encoded tree data with the third encoded tree data or the first encoded environment data with the third encoded environment data to generate one or more comparison data; and

determine whether the tree is in the environment based at least in part upon the one or more comparison data.

9. The computing device of claim 8 , wherein the instructions that cause the one or more processors to determine whether the tree is in the environment based at least in part upon the one or more comparison data further cause the one or more processors to:

validate that the tree is in the environment in response to the one or more comparison data satisfying one or more predetermined conditions.

10. The computing device of claim 8 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:

receive the first image depicting the tree;

select the first patch of the first image that depicts the part of the tree;

encode the first patch of the first image to generate the first encoded tree data;

encode the third patch of the third image to generate the third encoded tree data; and

compare the first encoded tree data with the third encoded tree data to generate the one or more comparison data.

11. The computing device of claim 8 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:

receive, the second image depicting the environment;

select the second patch of the second image that depicts the part of the environment;

encode the second patch of the second image to generate the first encoded environment data;

encode the third patch of the third image to generate the third encoded environment data; and

compare the first encoded environment data with the third encoded environment data to generate the one or more comparison data.

12. The computing device of claim 8 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:

receive the first image depicting the tree;

receive the second image depicting the environment;

select the first patch of the first image that depicts the part of the tree;

select the second patch of the second image that depicts the part of the environment;

encode the first patch of the first image to generate the first encoded tree data;

encode the third patch of the third image to generate the third encoded tree data;

compare the first encoded tree data with the third encoded tree data to generate one or more comparison tree data;

encode the second patch of the second image to generate the first encoded environment data;

encode the third patch of the third image to generate the third encoded environment data;

compare the first encoded environment data with the third encoded environment data to generate one or more comparison environment data; and

determine whether the tree is in the environment based at least in part upon the one or more comparison tree data and the one or more comparison environment data.

13. The computing device of claim 12 , wherein the instructions that cause the one or more processors to determine whether the tree is in the environment based at least in part upon the one or more comparison tree data and the one or more comparison environment data further cause the one or more processors to:

validate that the tree is in the environment in response to the one or more comparison tree data satisfying one or more predetermined tree conditions and the one or more comparison environment data satisfying one or more predetermined environment conditions.

14. The computing device of claim 8 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:

capture one or more images associated with whether the tree is in the environment.

15. A non-transitory computer-readable medium storing instructions, the instructions when executed by one or more processors of a computing device, cause the computing device to:

select a first patch of a first image that depicts a part of a tree or a second patch of a second image that depicts a part of an environment, and a third patch of a third image that depicts the part of the tree and the part of the environment;

encode the first patch of the first image to generate first encoded tree data or the second patch of the second image to generate first encoded environment data, and the third patch of the third image to generate third encoded tree data or third encoded environment data, wherein encoding the first patch or the second patch involves learning to generate the first encoded tree data or the first encoded environment data, respectively, based upon a model, wherein the model is an artificial neural network;

analyze the first patch or the second patch, wherein analyzing the first patch or the second patch includes performing one or more of feature extraction or applying pattern recognition;

use an autoencoder to generate the first encoded tree data or the first encoded environment data, wherein the autoencoder includes a reduction layer for inputs, one or more intermediate layers, and a reconstruction layer for outputs;

compare the first encoded tree data with the third encoded tree data or the first encoded environment data with the third encoded environment data to generate one or more comparison data; and

determine whether the tree is in the environment based at least in part upon the one or more comparison data.

16. The non-transitory computer-readable medium of claim 15 , wherein the instructions that when executed by the one or more processors cause the computing device to determine whether the tree is in the environment based at least in part upon the one or more comparison data further cause the computing device to:

validate that the tree is in the environment in response to the one or more comparison data satisfying one or more predetermined conditions.

17. The non-transitory computer-readable medium of claim 15 , wherein the instructions, when executed by the one or more processors, further cause the computing device to:

receive the first image depicting the tree;

select the first patch of the first image that depicts the part of the tree;

encode the first patch of the first image to generate the first encoded tree data;

encode the third patch of the third image to generate the third encoded tree data; and

compare the first encoded tree data with the third encoded tree data to generate the one or more comparison data.

18. The non-transitory computer-readable medium of claim 15 , wherein the instructions, when executed by the one or more processors, further cause the computing device to:

receive, the second image depicting the environment;

select the second patch of the second image that depicts the part of the environment;

encode the second patch of the second image to generate the first encoded environment data;

encode the third patch of the third image to generate the third encoded environment data; and

compare the first encoded environment data with the third encoded environment data to generate the one or more comparison data.

19. The non-transitory computer-readable medium of claim 15 , wherein the instructions, when executed by the one or more processors, further cause the computing device to:

receive the first image depicting the tree;

receive the second image depicting the environment;

select the first patch of the first image that depicts the part of the tree;

select the second patch of the second image that depicts the part of the environment;

encode the first patch of the first image to generate the first encoded tree data;

encode the third patch of the third image to generate the third encoded tree data;

compare the first encoded tree data with the third encoded tree data to generate one or more comparison tree data;

encode the second patch of the second image to generate the first encoded environment data;

encode the third patch of the third image to generate the third encoded environment data;

compare the first encoded environment data with the third encoded environment data to generate one or more comparison environment data; and

determine whether the tree is in the environment based at least in part upon the one or more comparison tree data and the one or more comparison environment data.

20. The non-transitory computer-readable medium of claim 19 , wherein the instructions that when executed by the one or more processors cause the computing device to determine whether the tree is in the environment based at least in part upon the one or more comparison tree data and the one or more comparison environment data further cause the computing device to:

validate that the tree is in the environment in response to the one or more comparison tree data satisfying one or more predetermined tree conditions and the one or more comparison environment data satisfying one or more predetermined environment conditions.

Assignments (2)
CHANGE OF NAME Recorded May 29, 2024
From: BLUEOWL, LLC
To: QUANATA, LLC
Reel/Frame 067558/0600 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 14, 2024
From: SANCHEZ, KENNETH JASON
To: BLUEOWL, LLC
Reel/Frame 067411/0926 →
Continuity (3)
Continuation PCTUS2021023639 · Mar 23, 2021
Provisional Application 63000874 · Mar 27, 2020
Related Publication 20230017596A1 · Jan 19, 2023
References Cited (91)
US 7103460B1 · Breed · 2006 [cited by applicant]
US 10127597B2 · Gebhardt et al. · 2018 [cited by applicant]
US 10445758B1 · Bryer et al. · 2019 [cited by applicant]
US 10830605B1 · Chintakindi et al. · 2020 [cited by applicant]
US 10915964B1 · Purgatorio et al. · 2021 [cited by applicant]
US 11341525B1 · Sanchez · 2022 [cited by applicant]
US 20020081997A1 · Morishima · 2002 [cited by applicant]
US 20030195694A1 · Kozak et al. · 2003 [cited by applicant]
US 20040039503A1 · Doyle · 2004 [cited by applicant]
US 20040049424A1 · Murray et al. · 2004 [cited by applicant]
US 20050154669A1 · Streetman · 2005 [cited by applicant]
US 20080046277A1 · Stamets · 2008 [cited by applicant]
US 20090043687A1 · Van Soestbergen et al. · 2009 [cited by applicant]
US 20090174703A1 · Hermanson et al. · 2009 [cited by applicant]
US 20090210295A1 · Edholm et al. · 2009 [cited by applicant]
US 20090292617A1 · Sperling et al. · 2009 [cited by applicant]
US 20100211260A1 · De Sanctis · 2010 [cited by applicant]
US 20110093321A1 · Streetman · 2011 [cited by applicant]
US 20110099489A1 · Glenn et al. · 2011 [cited by applicant]
US 20110106370A1 · Duddle et al. · 2011 [cited by applicant]
US 20110137763A1 · Aguilar · 2011 [cited by applicant]
US 20110184784A1 · Rudow · 2011 [cited by applicant]
US 20120150754A1 · Belady · 2012 [cited by applicant]
US 20130080053A1 · Rakshit · 2013 [cited by applicant]
US 20130218427A1 · Mukhopadhyay et al. · 2013 [cited by applicant]
US 20130218446A1 · Bradley · 2013 [cited by applicant]
US 20130282454A1 · Alpert et al. · 2013 [cited by applicant]
US 20140019179A1 · Gosalvez et al. · 2014 [cited by applicant]
US 20140040029A1 · Vhora · 2014 [cited by applicant]
US 20140081579A1 · Tyburski · 2014 [cited by applicant]
US 20140099000A1 · Shamir et al. · 2014 [cited by applicant]
US 20140107927A1 · Rojas · 2014 [cited by applicant]
US 20140129080A1 · Leibowitz · 2014 [cited by applicant]
US 20140180726A1 · Lavie et al. · 2014 [cited by applicant]
US 20140354630A1 · Kim et al. · 2014 [cited by applicant]
US 20140365096A1 · Cooper et al. · 2014 [cited by applicant]
US 20150148005A1 · Chau et al. · 2015 [cited by applicant]
US 20150206248A1 · Kornweibel et al. · 2015 [cited by applicant]
US 20150276419A1 · Hashem et al. · 2015 [cited by applicant]
US 20150371251A1 · Limbasia et al. · 2015 [cited by applicant]
US 20160034910A1 · Davis · 2016 [cited by applicant]
US 20160232550A1 · Fletcher et al. · 2016 [cited by applicant]
US 20160292768A1 · Needham · 2016 [cited by applicant]
US 20160343037A1 · Nicholas et al. · 2016 [cited by applicant]
US 20170069016A1 · Rackley et al. · 2017 [cited by applicant]
US 20170151917A1 · Bradley et al. · 2017 [cited by applicant]
US 20170214760A1 · Lee et al. · 2017 [cited by applicant]
US 20170351978A1 · Bellowe · 2017 [cited by applicant]
US 20180033352A1 · Kufus · 2018 [cited by applicant]
US 20180285885A1 · Laberteaux et al. · 2018 [cited by applicant]
US 20190026364A1 · Sankovsky · 2019 [cited by applicant]
US 20190026788A1 · Garrity et al. · 2019 [cited by applicant]
US 20190162154A1 · Pompea et al. · 2019 [cited by applicant]
US 20190213097A1 · Jin et al. · 2019 [cited by applicant]
US 20190303668A1 · King · 2019 [cited by examiner]
US 20200027102A1 · Xu et al. · 2020 [cited by applicant]
US 20200029490A1 · Bertucci et al. · 2020 [cited by applicant]
US 20200041290A1 · Schmelzer et al. · 2020 [cited by applicant]
US 20200074266A1 · Peake et al. · 2020 [cited by applicant]
US 20200074492A1 · Scholl et al. · 2020 [cited by applicant]
US 20200103892A1 · Cella et al. · 2020 [cited by applicant]
US 20200159377A1 · Joyce · 2020 [cited by examiner]
US 20200193739A1 · Gintz et al. · 2020 [cited by applicant]
US 20200200649A1 · Ammoura et al. · 2020 [cited by applicant]
US 20200211122A1 · Chen et al. · 2020 [cited by applicant]
US 20200242513A1 · Blayvas et al. · 2020 [cited by applicant]
US 20210010816A1 · Schmelzer et al. · 2021 [cited by applicant]
US 20210256781A1 · Jörn · 2021 [cited by applicant]
US 20210349475A1 · O'Regan et al. · 2021 [cited by applicant]
US 20220189482A1 · Penilla et al. · 2022 [cited by applicant]
US 20230016696A1 · Sanchez · 2023 [cited by applicant]
US 20230186878A1 · Wipperfürth · 2023 [cited by applicant]
US 20230245009A1 · Roy · 2023 [cited by applicant]
AU 2020100151 · 2020 [cited by applicant]
WO 2017219121A2 · 2017 [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US21/23555, mailed on Jun. 8, 2021, 15 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US21/23556, mailed on Jun. 8, 2021, 11 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US21/23579, mailed on Jun. 8, 2021, 9 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US21/23621, mailed on Jun. 10, 2021, 8 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US21/23626, mailed on Jun. 9, 2021, 8 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US21/23628, mailed on Jun. 9, 2021, 8 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US21/23634, mailed on Jun. 4, 2021, 12 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US21/23639, mailed on Jun. 7, 2021, 8 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US21/23645, mailed on Jun. 9, 2021, 8 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/US21/23668, mailed on Jun. 9, 2021, 11 pages. [cited by applicant]
B Libai et al. Setting Referral Fees in Affiliate Marketing. Journal of Service Research. 2003. [retrieved from internet on Apr. 8, 2023] <URL: https ://www.runi.ac. il/yedion/facultypublications/getfile .ashx?username=… [cited by applicant]
Committee on Trade and Investment. Life Cycle Assessment Best Practices of ISO 14040 Series. Retrieved from https://www.apec.org.docs/default-source/Publications/2004/2/Life-Cycle-Assessment-Best-Practices-of-Internatio… [cited by applicant]
American Heritage Dictionary, Driving, Print date Oct. 7, 2023, https://www.ahdictionary.com/word/search.html?q=driving 2023. [cited by applicant]
American Heritage Dictionary, Telematics, Print date Oct. 7, 2023 https://www.ahdictionary.com/word/search.html?q=telematics 2023. [cited by applicant]
Dunkle Werner, K., Drive Behavior and Fuel Efficiency. University of Michigan. Retrieved from https://deepblue-lib-umich.edu/bitstream/handle/2027.42.798892/karldw.pdf Apr. 5, 2013. [cited by applicant]
Energy Information Administration. Methodologies for Estimating Fuel Consumption Using the 2009 National Household Travel Survey, retrieved from https://nhts.ornl.gov/2009/pub/EIA.pdf Mar. 2011. [cited by applicant]