IP Library Granted Patent US 12,403,505
Granted Patent B2
US 12,403,505 · App. 18/590,841 · Granted Sep 2, 2025

Sorting of aluminum alloys

Inventors: Nalin Kumar (Fort Wayne, IN); Manuel Gerardo Garcia, Jr. (Austin, TX)
Assignee: SORTERA TECHNOLOGIES, INC.
B07C5/3422B07C5/34B07C5/342B07C5/04B07C2501/0054
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,403,505
App. No.
18/590,841
Granted
Sep 2, 2025
Kind
B2
Abstract

A material handling system sorts mixed materials utilizing a combination of a spectroscopic sensor, such as x-ray fluorescence, and a vision system that implements an artificial intelligence system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification. The system is capable of sorting between materials typically found within Zorba, such as wrought, cast, and extrusion aluminum alloys.

Claims (58)

1. A method for sorting of material pieces from a conveyed stream of materials, comprising:

performing one or more vision checks on each material piece within the conveyed stream of materials, wherein each of the one or more vision checks comprises classifying each material piece as a function of processing visual images captured from each material piece through an artificial intelligence (“AI”) system;

performing one or more sensor system classifications on each material piece within the conveyed stream of materials; and

sorting material pieces from the conveyed stream of materials into one or more classification groups as a function of a combination of the one or more vision checks and the one or more sensor system classifications.

2. The method as recited in claim 1 , wherein the conveyed stream of materials comprises one or more wrought aluminum alloys and one or more cast aluminum alloys, wherein the sorting further comprises:

sorting from the conveyed stream of materials one or more of the wrought aluminum alloys into one or more first classification groups based on a first combination of the one or more vision checks and the one or more sensor system classifications; and

sorting from the conveyed stream of materials one or more of the cast aluminum alloys into one or more second classification groups based on a second combination of the one or more vision checks and the one or more sensor system classifications,

wherein the sorting from the conveyed stream of materials of the one or more wrought aluminum alloys is performed before the sorting from the conveyed stream of materials of the one or more cast aluminum alloys.

3. The method as recited in claim 2 , wherein the first combination comprises a vision check to determine whether a material piece is composed of a wrought aluminum alloy and one or more sensor system classifications based on measured amounts of copper and zinc in the material piece.

4. The method as recited in claim 2 , wherein the sorting from the conveyed stream of materials one or more of the wrought aluminum alloys into one or more first classification groups further comprises sorting a material piece from the stream of material pieces as classified as a 2xxx series wrought aluminum alloy when a vision check determines that the material piece is composed of a wrought aluminum alloy and a sensor system classification determines that (1) a ratio of a measured amount of copper to a measured amount of zinc in the material piece is greater than a first predetermined value and (2) a measured amount of copper in the material piece is greater than a second predetermined value.

5. The method as recited in claim 2 , wherein the sorting from the conveyed stream of materials one or more of the wrought aluminum alloys into one or more first classification groups further comprises sorting a material piece from the stream of material pieces as classified as a 7xxx series wrought aluminum alloy when a vision check determines that the material piece is composed of a wrought aluminum alloy and a sensor system classification determines that (1) a ratio of a measured amount of copper to a measured amount of zinc in the material piece is less than a first predetermined value and (2) a measured amount of zinc in the material piece is greater than a second predetermined value.

6. The method as recited in claim 2 , wherein the sorting from the conveyed stream of materials one or more of the wrought aluminum alloys into one or more first classification groups further comprises sorting a material piece from the stream of material pieces as classified as a 3xxx and/or 5xxx and/or 6xxx series wrought aluminum alloy when:

(1) a vision check determines that the material piece is composed of a wrought aluminum alloy, and

(2) a first one of the one or more sensor system classifications determines that:

(i) a ratio of a measured amount of copper to a measured amount of zinc in the material piece is not greater than a first predetermined value, and

(ii) a measured amount of copper in the material piece is not greater than a second predetermined value, and

(3) a second one of the one or more sensor system classifications determines that:

(i) the ratio of a measured amount of copper to a measured amount of zinc in the material piece is not less than a third predetermined value, and

(ii) a measured amount of zinc in the material piece is not greater than a fourth predetermined value.

7. The method as recited in claim 2 , further comprising sorting from the conveyed stream of materials extrusion aluminum alloys into one or more third classification groups based on a third combination of the one or more vision checks, wherein the sorting from the conveyed stream of materials of the one or more extrusion aluminum alloys is performed subsequent to the sorting from the conveyed stream of materials of the one or more wrought aluminum alloys and before the sorting from the conveyed stream of materials of the one or more cast aluminum alloys.

8. The method as recited in claim 3 , wherein the sorting from the conveyed stream of materials one or more of the cast aluminum alloys into one or more second classification groups further comprises:

sorting a material piece from the stream of material pieces as classified as a 360 cast aluminum alloy when the first vision check determines that the material piece is not composed of a wrought aluminum alloy and a first one of the one or more sensor system classifications determines that a total measured amount of copper and zinc in the material piece is not greater than a first predetermined value and a second one of the one or more sensor system classifications determines that a measured amount of iron in the material piece is greater than a second predetermined value; and

sorting a material piece from the stream of material pieces as classified as a 356 cast aluminum alloy when the first vision check determines that the material piece is not composed of a wrought aluminum alloy and the first one of the one or more sensor system classifications determines that the total measured amount of copper and zinc in the material piece is not greater than the first predetermined value and the second one of the one or more sensor system classifications determines that the measured amount of iron in the material piece is not greater than the second predetermined value.

9. The method as recited in claim 8 , further comprising sorting a material piece from the stream of material pieces as classified as a die cast zinc piece when:

(1) the first vision check determines that the material piece is not composed of a wrought aluminum alloy, and

(2) the first one of the one or more sensor system classifications determines that a total measured amount of copper and zinc in the material piece is greater than the first predetermined value, and

(3) a third one of the one or more sensor system classifications determines that a ratio of a measured amount of copper to a measured amount of zinc in the material piece is less than a third predetermined value.

10. The method as recited in claim 2 , wherein the second combination comprises a vision check to determine whether a material piece is composed of a cast aluminum alloy and one or more sensor system classifications based on measured amounts of copper and zinc in the material piece.

11. The method as recited in claim 10 , wherein the sorting from the conveyed stream of materials one or more of the cast aluminum alloys into one or more second classification groups further comprises sorting a material piece from the stream of material pieces as classified as a 38x cast aluminum alloy when:

(1) the vision check determines that the material piece is composed of a cast aluminum alloy, and

(2) a first one of the one or more sensor system classifications determines that a total measured amount of copper and zinc in the material piece is greater than a first predetermined value, and

(3) a second one of the one or more sensor system classifications determines that a ratio of a measured amount of copper to a measured amount of zinc in the material piece is greater than a second predetermined value and less than a third predetermined value.

12. The method as recited in claim 11 , wherein the sorting from the conveyed stream of materials one or more of the cast aluminum alloys into one or more second classification groups further comprises sorting a material piece from the stream of material pieces as classified as a 319 cast aluminum alloy when:

(1) the vision check determines that the material piece is composed of a cast aluminum alloy, and

(2) the first one of the one or more sensor system classifications determines that the total measured amount of copper and zinc in the material piece is greater than the first predetermined value, and

(3) a third one of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the material piece is greater than the third predetermined value and less than a fourth predetermined value.

13. The method as recited in claim 12 , wherein the second combination also includes a vision check to determine whether the material piece is composed of a wrought aluminum alloy, which is performed before the vision check to determine whether the material piece is composed of a cast aluminum alloy.

14. The method as recited in claim 2 , wherein the conveyed stream of materials comprises Zorba materials, the method further comprising sorting from the conveyed stream of materials one or more junk materials based on one or more vision checks, wherein the sorting of the junk materials is performed before the sorting of the one or more wrought aluminum alloys and the sorting of the one or more cast aluminum alloys.

15. The method as recited in claim 2 , wherein each of the one or more sensor system classifications is performed by a spectroscopic system.

16. The method as recited in claim 15 , wherein the spectroscopic system is an x-ray fluorescence system.

17. The method as recited in claim 2 , wherein each of the one or more vision checks is performed by a single vision system implementing one or more AI models within the AI system, and wherein each of the one or more sensor system classifications is performed by a single spectroscopic system implementing one or more algorithms for analyzing spectral data collected from the material pieces.

18. A method for sorting of material pieces from a conveyed stream of Zorba materials, comprising:

performing vision checks on each material piece within the conveyed stream of Zorba materials, wherein each of the vision checks comprises classifying each material piece as a function of processing visual images captured from each material piece through an artificial intelligence (“AI”) system, wherein the vision checks are performed by a single vision system implementing a different AI model within the AI system for each of the vision checks;

performing sensor system classifications on each material piece within the conveyed stream of Zorba materials, wherein each of the sensor system classifications is performed by a different algorithm analyzing spectral data collected from each material piece by a single spectroscopic system;

sorting from the conveyed stream of Zorba materials a plurality of different wrought aluminum alloy material pieces into separately sorted classification groups based on a first combination of one or more vision checks and one or more sensor system classifications; and

sorting from the conveyed stream of Zorba materials a plurality of different cast aluminum alloys material pieces into separately sorted classification groups based on a second combination of one or more vision checks and one or more sensor system classifications.

19. The method as recited in claim 18 , wherein the sorting from the conveyed stream of Zorba materials of the plurality of different wrought aluminum alloy material pieces is performed before the sorting from the conveyed stream of Zorba materials of the plurality of different cast aluminum alloys material pieces, and wherein the single spectroscopic system is an x-ray fluorescence system.

20. The method as recited in claim 18 , wherein the sensor system classifications are based on measured amounts of copper and zinc in the material piece.

21. The method as recited in claim 18 , wherein the sorting from the conveyed stream of Zorba materials a plurality of different wrought aluminum alloy material pieces into separately sorted classification groups further comprises:

sorting a material piece from the conveyed stream of Zorba materials as classified as a 2xxx series wrought aluminum alloy when a vision check determines that the material piece is composed of a wrought aluminum alloy and a first sensor system classification determines that a ratio of a measured amount of copper to a measured amount of zinc in the material piece is greater than a first predetermined value and a measured amount of copper in the material piece is greater than a second predetermined value;

sorting a material piece from the conveyed stream of Zorba materials as classified as a 7xxx series wrought aluminum alloy when the vision check determines that the material piece is composed of a wrought aluminum alloy and a second sensor system classification determines that the ratio of a measured amount of copper to a measured amount of zinc in the material piece is less than a third predetermined value and a measured amount of zinc in the material piece is greater than a fourth predetermined value; and

sorting a material piece from the conveyed stream of Zorba materials as classified as a 3xxx and/or 5xxx and/or 6xxx series wrought aluminum alloy when the second sensor system classification determines that the ratio of a measured amount of copper to a measured amount of zinc in the material piece is not less than the third predetermined value and the measured amount of zinc in the material piece is not greater than the fourth predetermined value.

22. The method as recited in claim 18 , wherein the sorting from the conveyed stream of Zorba materials a plurality of different cast aluminum alloys material pieces into separately sorted classification groups further comprises:

sorting a material piece from the conveyed stream of Zorba materials as classified as a 360 cast aluminum alloy when a vision check determines that the material piece is composed of a cast aluminum alloy, a first sensor system classification determines that a total measured amount of copper and zinc in the material piece is less than a first predetermined value, and a second system classification determines that a measured amount of iron in the material piece is greater than a second predetermined value;

sorting a material piece from the conveyed stream of Zorba materials as classified as a 356 cast aluminum alloy when the vision check determines that the material piece is composed of a cast aluminum alloy, the first sensor system classification determines that the total measured amount of copper and zinc in the material piece is less than the first predetermined value, and the second system classification determines that the measured amount of iron in the material piece is less than the second predetermined value;

sorting a material piece from the conveyed stream of Zorba materials as classified as a 38x cast aluminum alloy when the vision check determines that the material piece is composed of a cast aluminum alloy, the first sensor system classification determines that a total measured amount of copper and zinc in the material piece is greater than a first predetermined value, and a third sensor system classification determines that a ratio of a measured amount of copper to a measured amount of zinc in the material piece is greater than a third predetermined value and less than a fourth predetermined value; and

sorting a material piece from the conveyed stream of Zorba materials as classified as a 319 cast aluminum alloy when the vision check determines that the material piece is composed of a cast aluminum alloy, the first sensor system classification determines that the total measured amount of copper and zinc in the material piece is greater than the first predetermined value, and a fourth sensor system classification determines that the ratio of the measured amount of copper to the measured amount of zinc in the material piece is greater than the fourth predetermined value and less than a fifth predetermined value.

23. The method as recited in claim 22 , further comprising sorting a material piece from the conveyed stream of Zorba materials as classified as a die cast zinc piece when the vision check determines that the material piece is composed of a cast aluminum alloy, the first sensor system classification determines that the total measured amount of copper and zinc in the material piece is greater than the first predetermined value, and a fifth sensor system classification determines that the ratio of a measured amount of copper to a measured amount of zinc in the material piece is less than the third predetermined value.

Continuity (15)
Continuation In Part 17495291 · Oct 6, 2021
Continuation In Part 17491415 · Sep 30, 2021
Continuation In Part 17380928 · Jul 20, 2021
Continuation In Part 17227245 · Apr 9, 2021
Continuation In Part 16939011 · Jul 26, 2020
Continuation In Part 16852514 · Apr 19, 2020
Continuation 16375675 · Apr 4, 2019
Division 16358374 · Mar 19, 2019
Continuation In Part 15963755 · Apr 26, 2018
Continuation In Part 15963755 · Apr 26, 2018
Continuation In Part 15213129 · Jul 18, 2016
Provisional Application 63487583 · Feb 28, 2023
Provisional Application 62490219 · Apr 26, 2017
Provisional Application 62193332 · Jul 16, 2015
Related Publication 20240246117A1 · Jul 25, 2024
References Cited (188)
US 4317521A · Clark · 1982 [cited by applicant]
US 4572735A · Poetzschke · 1986 [cited by applicant]
US 4586613A · Horii · 1986 [cited by applicant]
US 4834870A · Osterberg · 1989 [cited by applicant]
US 4848590A · Kelly · 1989 [cited by applicant]
US 5016039A · Sosa · 1991 [cited by examiner]
US 5042947A · Potzschke · 1991 [cited by applicant]
US 5114230A · Pryor · 1992 [cited by applicant]
US 5236092A · Krotkov · 1993 [cited by applicant]
US 5260576A · Sommer, Jr. · 1993 [cited by applicant]
US 5663997A · Willis · 1997 [cited by applicant]
US 5676256A · Kumar · 1997 [cited by applicant]
US 5733592A · Wettstein · 1998 [cited by applicant]
US 5738224A · Sommer, Jr. · 1998 [cited by applicant]
US 5911327A · Tanaka · 1999 [cited by applicant]
US 6064476A · Goltsos · 2000 [cited by applicant]
US 6100487A · Schultz · 2000 [cited by applicant]
US 6124560A · Roos · 2000 [cited by examiner]
US 6266390B1 · Sommer, Jr. · 2001 [cited by applicant]
US 6313422B1 · Anibas · 2001 [cited by applicant]
US 6313423B1 · Sommer · 2001 [cited by examiner]
US 6412642B2 · Charles · 2002 [cited by applicant]
US 6421042B1 · Omura · 2002 [cited by examiner]
US 6519315B2 · Sommer, Jr. · 2003 [cited by applicant]
US 6545240B2 · Kumar · 2003 [cited by applicant]
US 6795179B2 · Kumar · 2004 [cited by applicant]
US 7099433B2 · Sommer · 2006 [cited by applicant]
US 7200200B2 · Laurila · 2007 [cited by applicant]
US 7564943B2 · Sommer, Jr. · 2009 [cited by applicant]
US 7616733B2 · Sommer · 2009 [cited by applicant]
US 7674994B1 · Valerio · 2010 [cited by applicant]
US 7763820B1 · Sommer, Jr. · 2010 [cited by applicant]
US 7802685B2 · Allen · 2010 [cited by applicant]
US 7848484B2 · Sommer, Jr. · 2010 [cited by applicant]
US 7886915B2 · Shulman · 2011 [cited by applicant]
US 7978814B2 · Sommer · 2011 [cited by applicant]
US 8144831B2 · Sommer, Jr. · 2012 [cited by applicant]
US 8553838B2 · Sommer · 2013 [cited by applicant]
US 8600545B2 · Earlam · 2013 [cited by applicant]
US 8615123B2 · Dabic · 2013 [cited by examiner]
US 9156162B2 · Suzuki · 2015 [cited by applicant]
US 9316596B2 · Levesque · 2016 [cited by applicant]
US 9514590B2 · Lindbichler · 2016 [cited by applicant]
US 9785851B1 · Torek · 2017 [cited by applicant]
US 9927354B1 · Starr · 2018 [cited by examiner]
US 9956609B1 · De Saro · 2018 [cited by applicant]
US 10005107B2 · Ogusu · 2018 [cited by applicant]
US 10036142B2 · Bamber · 2018 [cited by examiner]
US 10207296B2 · Garcia · 2019 [cited by examiner]
US 10295451B2 · Schneider · 2019 [cited by applicant]
US 10467477B2 · Gershtein · 2019 [cited by applicant]
US 10478861B2 · Comtois · 2019 [cited by applicant]
US 10625304B2 · Kumar · 2020 [cited by examiner]
US 10710119B2 · Kumar · 2020 [cited by examiner]
US 10722922B2 · Kumar · 2020 [cited by examiner]
US 10799915B2 · Horowitz · 2020 [cited by applicant]
US 10824936B2 · Wu · 2020 [cited by applicant]
US 10967404B2 · Grof · 2021 [cited by applicant]
US 11278937B2 · Kumar · 2022 [cited by examiner]
US 11964304B2 · Kumar · 2024 [cited by examiner]
US 12246355B2 · Kumar · 2025 [cited by examiner]
US 20020186882A1 · Cotman · 2002 [cited by applicant]
US 20030147494A1 · Sommer, Jr. · 2003 [cited by applicant]
US 20040151364A1 · Kenneway · 2004 [cited by applicant]
US 20040235970A1 · Smith · 2004 [cited by applicant]
US 20060239401A1 · Sommer · 2006 [cited by applicant]
US 20070029232A1 · Cowling · 2007 [cited by examiner]
US 20070262000A1 · Valerio · 2007 [cited by applicant]
US 20080029445A1 · Russcher · 2008 [cited by applicant]
US 20080257795A1 · Shuttleworth · 2008 [cited by applicant]
US 20080302707A1 · Bourely · 2008 [cited by applicant]
US 20090292422A1 · Eiswerth · 2009 [cited by applicant]
US 20100017020A1 · Hubbard-Nelson · 2010 [cited by examiner]
US 20100264070A1 · Sommer, Jr. · 2010 [cited by applicant]
US 20110017644A1 · Valerio · 2011 [cited by applicant]
US 20110247730A1 · Yanar · 2011 [cited by examiner]
US 20120148018A1 · Sommer, Jr. · 2012 [cited by applicant]
US 20130028487A1 · Stager · 2013 [cited by applicant]
US 20130079918A1 · Spencer · 2013 [cited by applicant]
US 20130126399A1 · Wolff · 2013 [cited by applicant]
US 20130184853A1 · Roos · 2013 [cited by examiner]
US 20130264249A1 · Sommer, Jr. · 2013 [cited by applicant]
US 20130304254A1 · Torek · 2013 [cited by examiner]
US 20150012226A1 · Skaff · 2015 [cited by examiner]
US 20150336135A1 · Corak · 2015 [cited by applicant]
US 20160016201A1 · Schons · 2016 [cited by examiner]
US 20160059450A1 · Meredith · 2016 [cited by applicant]
US 20160136694A1 · Janda · 2016 [cited by examiner]
US 20160250665A1 · Lampe · 2016 [cited by applicant]
US 20160346811A1 · Iino · 2016 [cited by applicant]
US 20170014868A1 · Garcia · 2017 [cited by applicant]
US 20170232479A1 · Pietzka · 2017 [cited by examiner]
US 20170261437A1 · Buchter · 2017 [cited by applicant]
US 20180065155A1 · Ripley · 2018 [cited by applicant]
US 20180243800A1 · Kumar · 2018 [cited by examiner]
US 20190130560A1 · Horowitz · 2019 [cited by applicant]
US 20190210067A1 · Sortera · 2019 [cited by applicant]
US 20190247891A1 · Sortera · 2019 [cited by applicant]
US 20190299255A1 · Chaganti · 2019 [cited by examiner]
US 20200050922A1 · Wu · 2020 [cited by applicant]
US 20200290088A1 · Sortera · 2020 [cited by applicant]
US 20200368786A1 · Sortera · 2020 [cited by applicant]
US 20210001377A1 · Sutton · 2021 [cited by applicant]
US 20210046509A1 · Andersen · 2021 [cited by applicant]
US 20210094075A1 · Horowitz et al. · 2021 [cited by applicant]
US 20210217156A1 · Balachandran et al. · 2021 [cited by applicant]
US 20210229133A1 · Sortera · 2021 [cited by applicant]
US 20210346916A1 · Sortera · 2021 [cited by applicant]
US 20220016675A1 · Sortera · 2022 [cited by applicant]
US 20220023918A1 · Sortera · 2022 [cited by applicant]
US 20220161298A1 · Sortera · 2022 [cited by applicant]
US 20220168781A1 · Sortera · 2022 [cited by applicant]
US 20220203407A1 · Sortera · 2022 [cited by applicant]
US 20220245402A1 · Tae · 2022 [cited by applicant]
US 20220355342A1 · Sortera · 2022 [cited by applicant]
US 20220371057A1 · Sortera · 2022 [cited by applicant]
US 20230011383A1 · Balthasar · 2023 [cited by applicant]
US 20230044783A1 · Sortera · 2023 [cited by applicant]
US 20230053268A1 · Sortera · 2023 [cited by applicant]
US 20230169751A1 · Geurts · 2023 [cited by applicant]
US 20230173543A1 · Sortera · 2023 [cited by applicant]
US 20230176028A1 · Sortera · 2023 [cited by applicant]
US 20240109103A1 · Kumar et al. · 2024 [cited by applicant]
US 20240149304A1 · Sortera · 2024 [cited by applicant]
BR PI02107945B1 · 2002 [cited by applicant]
CA 3065962A1 · 2020 [cited by examiner]
CN 102861722 · 2013 [cited by applicant]
CN 106000904 · 2016 [cited by applicant]
CN 107552412A · 2018 [cited by examiner]
CN 111659635 · 2020 [cited by applicant]
CN 211888005U · 2020 [cited by applicant]
DE 202009006383 · 2009 [cited by applicant]
EP 0074447 · 1987 [cited by applicant]
JP H07275802 · 1995 [cited by applicant]
JP 2010172799 · 2010 [cited by applicant]
JP 5969685 · 2016 [cited by applicant]
JP 2017109197 · 2017 [cited by applicant]
JP 2021163078 · 2021 [cited by applicant]
TW I707812 · 2020 [cited by applicant]
WO WO2011159269 · 2011 [cited by applicant]
WO WO2016199074 · 2016 [cited by applicant]
WO WO2017011835A1 · 2017 [cited by examiner]
WO WO2019241114A1 · 2019 [cited by examiner]
WO WO2021126876A1 · 2021 [cited by examiner]
WO WO2023137423 · 2023 [cited by applicant]
European Patent Office; Extended European Search Report for application EP19792330.3; Apr. 30, 2021; 7 pages. [cited by applicant]
India Patent Office; Office Action issued for India Application Serial No. 201937044046; Jun. 4, 2020; 7 pages. [cited by applicant]
United States International Searching Authority; International Search Report & Written Opinion for PCT/US2016/042850; Sep. 28, 2016; 15 pages. [cited by applicant]
A. Lee, “Comparing Deep Neural Networks and Traditional Vision Algorithms in Mobile Robotics,” Swarthmore College, 9 pages, downloaded from Internet on May 1, 2018. [cited by applicant]
K. Tarbell et al., “Applying Machine Learning to the Sorting of Recyclable Containers,” University of Illinois at Urbana-Champaign, Urbana, Illinois, 7 pages, downloaded from Internet on May 1, 2018. [cited by applicant]
The United States Patent and Trademark Office, Non-Final Office Action, U.S. Appl. No. 15/213,129, filed Oct. 6, 2017. [cited by applicant]
International Searching Authority, International Search Report and the Written Opinion, International Application No. PCT/US2018/029640, Jul. 23, 2018; 23 pages. [cited by applicant]
European Patent Office; Extended Search Report for application 16825313.6; Jan. 28, 2019; 12 pages. [cited by applicant]
India Patent Office; Office Action issued for India Application Serial No. 201817002365; Mar. 12, 2020; 6 pages. [cited by applicant]
International Searching Authority, International Search Report and the Written Opinion, International Application No. PCT/US2019/022995, Jun. 5, 2019; 10 pages. [cited by applicant]
The United States Patent and Trademark Office, Non-Final Office Action, U.S. Appl. No. 16/375,675, filed Jun. 28, 2019. [cited by applicant]
The United States Patent and Trademark Office, Final Office Action, U.S. Appl. No. 16/375,675, filed Jan. 17, 2020. [cited by applicant]
Chinese Patent Office; Office Action issued for Chinese Application No. 201980043725.X on Apr. 28, 2022; 21 pages; Beijing, CN. [cited by applicant]
Zhou et al., “SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection,” ArXiv: 1902.09080v3, Jun. 6, 2019, pp. 4321-4330. [cited by applicant]
Zhang et al., “Designing and verifying a disassembly line approach to cope with the upsurge of end-of-life vehicles in China,” Elsevier, Waste Management (2018), vol. 76, Jun. 2018, pp. 697-707. Retrieved on Jul. 10, 20… [cited by applicant]
Japan Patent Office; Office Action issued Jan. 10, 2023 for Serial No. 2021-509947; 9 pages (with translation). [cited by applicant]
International Searching Authority, International Search Report and The Written Opinion of the International Searching Authority, International Application No. PCT/US2022/035011, Oct. 27, 2022. [cited by applicant]
Rozenstein, O. et al., “Development of a new approach based on midwave infrared spectroscopy for post-consumer black plastic waste sorting in the recycling industry,” Waste Management 68 (2017); pp. 38-44, Jul. 2017. [cited by applicant]
United States International Searching Authority; International Search Report & Written Opinion for PCT/US2022/020657; Jun. 16, 2022; 10 pages. [cited by applicant]
United States International Searching Authority; International Search Report & Written Opinion for PCT/US2022/016869; Jun. 29, 2022; 11 pages. [cited by applicant]
United States International Searching Authority; International Search Report & Written Opinion for PCT/US2022/060626; May 2, 2023; 12 pages. [cited by applicant]
United States International Searching Authority; International Search Report & Written Opinion for PCT/US2022/051681; Mar. 20, 2023; 6 pages. [cited by applicant]
United States International Searching Authority; International Search Report & Written Opinion for PCT/US2022/039622; Oct. 28, 2022; 12 pages. [cited by applicant]
United States International Searching Authority; International Search Report & Written Opinion for PCT/US2022/035013; Sep. 23, 2022; 7 pages. [cited by applicant]
Mach Vision New High Technology Equipment—Machinex, Jun. 8, 2021, downloaded from https://www.machinexrecycling.com/news/mach-vision-new-high-technology-equipment/, 1 page. [cited by applicant]
BHS and NRT Introduce Max-AITM; Bulk Handling Systems (BHS); Apr. 18, 2017; downloaded from https://max-ai.com/autonomous-qc/ on Apr. 18, 2024. [cited by applicant]
Gao et al., “Applying Improved Optical Recognition with Machine Learning on Sorting Cu Impurities in Steel Scrap,” Journal of Sustainable Metallurgy, vol. 6, pp. 785-795, Dec. 7, 2020. [cited by applicant]
United States International Searching Authority; International Search Report & Written Opinion for PCT/US2022/030943; Sep. 28, 2022; 10 pages; Alexandria, VA. [cited by applicant]
The United States Patent and Trademark Office, Non-Final Office Action, U.S. Appl. No. 16/358,374, filed Jun. 28, 2019. [cited by applicant]
The United States Patent and Trademark Office, Non-Final Office Action, U.S. Appl. No. 16/358,374, filed Dec. 12, 2019. [cited by applicant]
The United States Patent and Trademark Office, Non-Final Office Action, U.S. Appl. No. 15/963,755, filed Apr. 5, 2019. [cited by applicant]
The United States Patent and Trademark Office, Non-Final Office Action, U.S. Appl. No. 15/963,755, filed Sep. 13, 2019. [cited by applicant]
The United States Patent and Trademark Office, Non-Final Office Action, U.S. Appl. No. 15/963,755, filed May 11, 2020. [cited by applicant]
The United States Patent and Trademark Office, Non-Final Office Action, U.S. Appl. No. 17/491,415, filed Nov. 15, 2021. [cited by applicant]
The United States Patent and Trademark Office, Non-Final Office Action, U.S. Appl. No. 17/673,694, filed Jan. 25, 2024. [cited by applicant]
The United States Patent and Trademark Office, Non-Final Office Action, U.S. Appl. No. 17/696,831, filed Mar. 29, 2024. [cited by applicant]
The United States Patent and Trademark Office, Non-Final Office Action, U.S. Appl. No. 17/495,291, filed Oct. 27, 2023. [cited by applicant]
The United States Patent and Trademark Office, Non-Final Office Action, U.S. Appl. No. 17/972,507, filed Jun. 20, 2024. [cited by applicant]
United States International Searching Authority; International Search Report & Written Opinion for PCT/US2024/017756; Jun. 17, 2024; 12 pages. [cited by applicant]
United States International Searching Authority; International Search Report & Written Opinion for PCT/US2024/017762; Jun. 17, 2024; 15 pages. [cited by applicant]
Elmahi, E.M., “Studies of Some Alloys Using X-Ray Fluorescence,” Thesis; University of Khartoum, Jan. 1997, 58 pages. [cited by applicant]
Data Sheet for Aluminum 319.0-F, Sand Cast; available from MatWeb, www.matweb.com; downloaded from internet on Sep. 29, 2022; 2 pages. [cited by applicant]
Data Sheet for Aluminum 356.0-F, Sand Cast; available from MatWeb, www.matweb.com; downloaded from internet on Sep. 29, 2022; 1 page. [cited by applicant]