IP Library › Granted Patent US 12,400,462
Granted Patent B2
US 12,400,462 · App. 18/904,505 · Granted Aug 26, 2025

Methods and apparatus to analyze an image of a portion of an item for a patternindicating authenticity of the item

Inventors: Hideto Oda (Tokyo, JP); Dan Van Tran (Bedminster, NJ)
Assignee: Collectors Universe, Inc.
G06V20/95G06V10/50G06V10/7715G06V10/774G06V10/993
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 12,400,462
App. No.
18/904,505
Granted
Aug 26, 2025
Kind
B2
Abstract

In an embodiment, a method includes receiving a plurality of images having a plurality of image types. The method further includes, for each image type from the plurality of image types and to generate a plurality of subsets of images, identifying a subset of images from the plurality of images being that image type using an image classifier. The method further includes, for each subset of images from the plurality of subsets of images, performing feature extraction on each image from that subset of images to generate features associated with that image. The method further includes inputting the features associated with each image from that subset of images to a trained ML model from a plurality of trained ML models to generate an output indicating whether a collectible associated with that image is authentic or counterfeit.

Claims (63)

1. A non-transitory, processor-readable medium storing instructions that, when executed by a processor, cause the processor to:

receive a plurality of images having an image type, each image from the plurality of images including a predetermined portion, the plurality of images including a non-synthetic image and a synthetic image, the synthetic image generated by augmenting the predetermined portion of the non-synthetic image and not remaining portions of the non-synthetic image;

for each image from the plurality of images and to generate a plurality of sets of features associated with the plurality of images, perform feature extraction based on the predetermined portion of that image and not remaining portions of that image to generate a set of features associated with that image;

train, to generate a trained machine learning (ML) model, an ML model using the plurality of sets of features associated with the plurality of images;

receive an image (1) not included in the plurality of images, (2) that is the image type, and (3) has the predetermined portion;

perform feature extraction based on the predetermined portion of the image and not remaining portions of the image to generate a set of features associated with the image; and

input the set of features associated with the image to the trained ML model to generate an output indicating an authenticity of a collectible associated with the image.

2. The non-transitory, processor-readable medium of claim 1 , wherein the ML model is a one class support vector machine (SVM).

3. The non-transitory, processor-readable medium of claim 1 , wherein performing the feature extraction includes performing feature extraction via histogram of oriented gradients (HOG).

4. The non-transitory, processor-readable medium of claim 1 , wherein performing the feature extraction includes performing feature extraction via at least one of local binary pattern (LBP), local ternary pattern (LTP), local phase quantization (LPQ), local derivative ternary pattern (LDTP), gabor filters, or local phase congruency (LPC).

5. The non-transitory, processor-readable medium of claim 1 , wherein the plurality of images is a first plurality of images, the image type is a first image type, the trained ML model is a first trained ML model, the ML model is a first ML model, the image is a first image, and the non-transitory, processor-readable medium further stores instructions to cause the processor to:

receive a second plurality of images having a second image type different than the first image type, each image from the second plurality of images having the predetermined portion;

for each image from the second plurality of images and to generate a plurality of sets of features associated with the second plurality of images, perform feature extraction based on the predetermined portion of that image and not remaining portions of that image to generate a set of features associated with that image;

train, to generate a second trained ML model different than the first trained ML model, a second ML model using the plurality of sets of features associated with the second plurality of images;

receive a second image that is the second image type and has the predetermined portion;

perform feature extraction based on the predetermined portion of the second image and not remaining portions of the second image to generate a set of features associated with the second image; and

input the set of features associated with the second image to the second trained ML model but not the first trained ML model to generate an output indicating an authenticity of a collectible associated with the second image.

6. The non-transitory, processor-readable medium of claim 1 , wherein

the plurality of images includes images of authentic collectibles and images of counterfeit collectibles, and

training the ML model to generate the trained ML model is done via supervised learning.

7. The non-transitory, processor-readable medium of claim 1 , wherein

the plurality of images includes images of authentic collectibles and not images of counterfeit collectibles, and

training the ML model to generate the trained ML model is done via unsupervised learning.

8. The non-transitory, processor-readable medium of claim 1 , wherein the ML model includes at least one of an isolation forest model, a local outlier factor model, an autoencoder, a density-based model, or a statistical-based model.

9. The non-transitory, processor-readable medium of claim 1 , wherein the ML model includes at least one of a deep nearest neighbor anomaly detection model, a semantic pyramid anomaly detection (SPADE) model, a patch distribution modeling framework for anomaly detection and localization (PaDiM) model, an autoencoder, or a towards total recall in industrial anomaly detection (PatchCore) model.

10. The non-transitory, processor-readable medium of claim 1 , wherein the non-transitory, processor-readable medium further stores instructions to cause the processor to:

confirm, before at least one of the performing the feature extraction or the inputting the set of features, that a quality associated with the image is within a predetermined acceptable quality range, the predetermined acceptable quality range determined based on the trained ML model.

11. The non-transitory, processor-readable medium of claim 1 , wherein the image is a first image and the non-transitory, processor-readable medium further stores instructions to cause the processor to:

receive a second image (1) not included in the plurality of images, (2) that is the image type, and (3) has the predetermined portion;

determine that a quality associated with the second image is outside a predetermined acceptable quality range associated with the trained ML model; and

refrain, in response to determining that the quality associated with the second image is outside the predetermined acceptable quality range, from at least one of performing feature extraction based on the second image or inputting features associated with the second image into the trained ML model.

12. The non-transitory, processor-readable medium of claim 1 , wherein the image type is a first image type and the non-transitory, processor-readable medium of claim 1 further stores instructions that, when executed by the processor, cause the processor to:

identify the plurality of images having the first image type from a collection of images having a plurality of image types, the plurality of image types including the first image type and a second image type different than the first image type.

13. A method, comprising:

receive a first plurality of images having a first image type, each image from the first plurality of images including a predetermined portion;

for each image from the first plurality of images and to generate a plurality of sets of features associated with the first plurality of images, perform feature extraction based on the predetermined portion of that image and not remaining portions of that image to generate a set of features associated with that image;

receive a first image (1) not included in the first plurality of images, (2) that is the first image type, and (3) has the predetermined portion;

perform feature extraction based on the predetermined portion of the first image and not remaining portions of the first image to generate a set of features associated with the first image;

input the set of features associated with the first image to a first trained machine learning (ML) model to generate an output indicating an authenticity of an object associated with the first image, a first ML model trained using the plurality of sets of features associated with the first plurality of images to generate the first trained ML model;

receive a second plurality of images having a second image type different than the first image type, each image from the second plurality of images having the predetermined portion;

for each image from the second plurality of images and to generate a plurality of sets of features associated with the second plurality of images, perform feature extraction based on the predetermined portion of that image and not remaining portions of that image to generate a set of features associated with that image;

receive a second image (1) not included in the second plurality of images, (2) that is the second image type, and (3) has the predetermined portion;

perform feature extraction based on the predetermined portion of the second image and not remaining portions of the second image to generate a set of features associated with the second image; and

input the set of features associated with the second image to a second trained ML model but not the first trained ML model to generate an output indicating an authenticity of an object associated with the second image, a second ML model trained using the plurality of sets of features associated with the second plurality of images to generate the second trained ML model.

14. The method of claim 13 , wherein the first trained ML model is a one class support vector machine (SVM) and the performing the feature extraction based on the predetermined portion of the first image and not remaining portions of the first image includes performing feature extraction via histogram of oriented gradients (HOG).

15. The method of claim 13 , further comprising:

confirming, before at least one of (1) the performing the feature extraction based on the predetermined portion of the first image and not remaining portions of the first image or (2) the inputting the set of features associated with the first image to the first trained ML model, that a quality associated with the first image is within a predetermined acceptable quality range, the predetermined acceptable quality range determined based on the first trained ML model.

16. The method of claim 13 , further comprising:

identifying the first plurality of images having the first image type from a collection of images having a plurality of image types, the plurality of image types including the first image type and the second image type.

17. The method of claim 13 , wherein the first plurality of images includes a non-synthetic image of a card and a synthetic image of a card, the predetermined portion of the non-synthetic image of the card augmented to generate the synthetic image of the card.

18. An apparatus, comprising:

a memory; and

a processor operatively coupled to the memory, the processor configured to:

receive a first plurality of images having a first image type, each image from the first plurality of images including a predetermined portion;

for each image from the first plurality of images and to generate a plurality of sets of features associated with the first plurality of images, perform feature extraction based on the predetermined portion of that image and not remaining portions of that image to generate a set of features associated with that image;

receive a first image (1) not included in the first plurality of images, (2) that is the first image type, and (3) that has the predetermined portion;

perform feature extraction based on the predetermined portion of the first image and not remaining portions of the first image to generate a set of features associated with the first image;

input the set of features associated with the first image to a first trained machine learning (ML) model to generate an output indicating a status associated with the first image, a first ML model trained using the plurality of sets of features associated with the first plurality of images to generate the first trained ML model

receive a second plurality of images having a second image type, each image from the second plurality of images including the predetermined portion;

for each image from the first plurality of images and to generate a plurality of sets of features associated with the second plurality of images, perform feature extraction based on the predetermined portion of that image and not remaining portions of that image to generate a set of features associated with that image;

receive a second image (1) not included in the second plurality of images, (2) that is the second image type, and (3) that has the predetermined portion;

perform feature extraction based on the predetermined portion of the second image and not remaining portions of the second image to generate a set of features associated with the second image; and

input the set of features associated with the second image to a second trained ML model and not the first trained ML model to generate an output indicating a status associated with the second image, a second ML model trained using the plurality of sets of features associated with the second plurality of images to generate the second trained ML model.

Assignments (2)
SECURITY INTEREST Recorded Aug 22, 2025
From: COLLECTORS UNIVERSE, INC.
To: JPMORGAN CHASE BANK, N.A., AS ADMINISTRATIVE AGENT
Reel/Frame 072095/0824 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 1, 2024
From: ODA, HIDETO; TRAN, DAN VAN
To: COLLECTORS UNIVERSE, INC.
Reel/Frame 069105/0230 →
Continuity (2)
Provisional Application 63587280 · Oct 2, 2023
Related Publication 20250111685A1 · Apr 3, 2025
References Cited (166)
US 4899392A · Merton · 1990 [cited by applicant]
US 5133019A · Merton et al. · 1992 [cited by applicant]
US 5220614A · Crain · 1993 [cited by applicant]
US 5224176A · Crain · 1993 [cited by applicant]
US 6239867B1 · Aggarwal · 2001 [cited by applicant]
US 6295750B1 · Harwell et al. · 2001 [cited by applicant]
US 6726205B1 · Purton · 2004 [cited by applicant]
US 7119689B2 · Mallett et al. · 2006 [cited by applicant]
US 7660468B2 · Gokturk et al. · 2010 [cited by applicant]
US 8234185B2 · Davis · 2012 [cited by applicant]
US 8626600B2 · Yankovich et al. · 2014 [cited by applicant]
US 8977603B2 · Pate et al. · 2015 [cited by applicant]
US 9050719B2 · Valpola et al. · 2015 [cited by applicant]
US 9443298B2 · Ross et al. · 2016 [cited by applicant]
US 9538149B2 · Williams et al. · 2017 [cited by applicant]
US 9672551B2 · Pate et al. · 2017 [cited by applicant]
US 9679319B2 · Yankovich et al. · 2017 [cited by applicant]
US 9767163B2 · Kass et al. · 2017 [cited by applicant]
US 10104197B2 · Williams et al. · 2018 [cited by applicant]
US 10146841B2 · Kass et al. · 2018 [cited by applicant]
US 10229445B2 · Pate et al. · 2019 [cited by applicant]
US 10360531B1 · Stallman et al. · 2019 [cited by applicant]
US 10445330B2 · Kass et al. · 2019 [cited by applicant]
US 10459931B2 · Kass et al. · 2019 [cited by applicant]
US 10470740B2 · Freudenberger et al. · 2019 [cited by applicant]
US 10500735B1 · Menon et al. · 2019 [cited by applicant]
US 10525599B1 · Zutshi · 2020 [cited by applicant]
US 10529137B1 · Black · 2020 [cited by examiner]
US 10561469B2 · Kasai et al. · 2020 [cited by applicant]
US 10630805B2 · Williams et al. · 2020 [cited by applicant]
US 10753882B1 · Mahajan et al. · 2020 [cited by applicant]
US 10942933B2 · Kass et al. · 2021 [cited by applicant]
US 12141817B2 · Frisbee et al. · 2024 [cited by applicant]
US 12159393B2 · Shalamberidze et al. · 2024 [cited by applicant]
US 20050197853A1 · Ueno · 2005 [cited by applicant]
US 20070187266A1 · Porter et al. · 2007 [cited by applicant]
US 20070279494A1 · Aman et al. · 2007 [cited by applicant]
US 20080023343A1 · Macor · 2008 [cited by applicant]
US 20080023351A1 · Macor · 2008 [cited by applicant]
US 20100088168A1 · Sullivan et al. · 2010 [cited by applicant]
US 20140083243A1 · Morrow · 2014 [cited by applicant]
US 20140279527A1 · Duke et al. · 2014 [cited by applicant]
US 20150117701A1 · Ross et al. · 2015 [cited by applicant]
US 20160210734A1 · Kass et al. · 2016 [cited by applicant]
US 20170032285A1 · Sharma et al. · 2017 [cited by applicant]
US 20170308744A1 · Gaubatz · 2017 [cited by examiner]
US 20170343481A1 · Jahanshahi et al. · 2017 [cited by applicant]
US 20180268378A1 · Liu et al. · 2018 [cited by applicant]
US 20190130560A1 · Horowitz et al. · 2019 [cited by applicant]
US 20190205959A1 · Pate et al. · 2019 [cited by applicant]
US 20190392457A1 · Kuntagod et al. · 2019 [cited by applicant]
US 20200082522A1 · Bonneau et al. · 2020 [cited by applicant]
US 20200193666A1 · Cinnamon et al. · 2020 [cited by applicant]
US 20200193866A1 · Kubota et al. · 2020 [cited by applicant]
US 20210042797A1 · Shamiss et al. · 2021 [cited by applicant]
US 20210065353A1 · Potter et al. · 2021 [cited by applicant]
US 20210158274A1 · Patchen · 2021 [cited by applicant]
US 20210201039A1 · Frei et al. · 2021 [cited by applicant]
US 20210304559A1 · Cupersmith et al. · 2021 [cited by applicant]
US 20220012446A1 · Dolmayan · 2022 [cited by applicant]
US 20220036371A1 · Frisbee et al. · 2022 [cited by applicant]
US 20220092609A1 · Giera · 2022 [cited by examiner]
US 20220261984A1 · Shalamberidze et al. · 2022 [cited by applicant]
US 20220343483A1 · Desai · 2022 [cited by applicant]
US 20220374946A1 · Kass et al. · 2022 [cited by applicant]
US 20230191823A1 · Hsu · 2023 [cited by examiner]
US 20230252532A1 · Isakov et al. · 2023 [cited by applicant]
US 20240005688A1 · Douglas · 2024 [cited by examiner]
US 20240037385A1 · Torabi · 2024 [cited by examiner]
US 20240066910A1 · Kass et al. · 2024 [cited by applicant]
KR 20130061567A · 2013 [cited by applicant]
WO WO2015080669A1 · 2015 [cited by applicant]
WO WO2022026924A1 · 2022 [cited by applicant]
WO WO2022178270A1 · 2022 [cited by applicant]
Alake, R., “Deep Learning: Understanding The Inception Module,” Towards Data Science, Published on Dec. 22, 2020. Retrieved online from https://towardsdatascience.com/deep-learning-understand-the-inception-module-561468… [cited by applicant]
Author Unknown, “ CoinManage 2006: Getting Started Guide,” Liberty Street Software, 2006; 53 pages. [cited by applicant]
Author Unknown, “2012 Topps Classic Walk-Offs, Professional Sports Authenticator,” Sep. 15, 2015, Retrieved online from https://web.archive.org/web/20150915013815/http:/www.psacard.com/Pop/Detail.aspx?c=102355; 1 page. [cited by applicant]
Author Unknown, “Computerized Grading?” User RNICK on Apr. 22, 2004, in Trading Cards & Memorabilia Forum, Collectors Universe, Retrieved online from https://forums.collectors.com/discussion/comment/2670306/#Comment_267… [cited by applicant]
Author Unknown, “Diamonds. The 4 C's of a diamond,” Goldsmith Jewelers, Jan. 10, 2007, Retrieved online from https://web.archive.org/web/20070110234016/http:/www.goldsmithlf.com/Diamonds.html; 1 page. [cited by applicant]
Author Unknown, “Gemology 101,” Mardon Jewelers: at the Mission Inn, Oct. 13, 2010, Retrieved online from https://web.archive.org/web/20101013162932/https:/www.mardonjewelers.com/gemstones/gemology-101.php; 1 page. [cited by applicant]
Author Unknown “Imagenet Overview,” 2016, Stanford Vision Lab, Stanford University, Princeton University, https://web.archive.org/web/20210125211537/http://image-net.org/about-overview; 1 page. [cited by applicant]
Author Unknown, “PCGS: Reconsideration. You asked for it and PCGS delivered.” Professional Coin Grading Service, Oct. 31, 2013, Retrieved online from https://web.archive.org/web/20131031073907/https:/www.pcgs.com/recons… [cited by applicant]
Author Unknown, “Population Report, Professional Sports Authenticator,” Feb. 10, 2013, Retrieved online from https://web.archive.org/web/20130210162234/http:/www.psacard.com/POP/Default.aspx; 1 page. [cited by applicant]
Author Unknown, “Population Report, Professional Sports Authenticator,” Feb. 18, 2013, Retrieved online from https://web.archive.org/web/20130218141125/http:/www.psacard.com/pop/SubCategory.aspx?c=20003; 3 pages. [cited by applicant]
Author Unknown, “PSA Offers Easy Way to Sell Set Registry Cards via Collectors Corner,” Professional Sports Authenticator. Published on May 15, 2014. Retrieved online from https://www.psacard.com/articles/articleview/81… [cited by applicant]
Author Unknown “PSA Security: A Buyer's Guide, ” Professional Sports Authenticator, Dec. 3, 2014, Retrieved online from https://web.archive.org/web/20141203050146/https:/www.psacard.com/services/psasecurityabuyersguide,… [cited by applicant]
Author Unknown “The World's FIRST Online Grading Service,” Online Grading Services, LLC, Dec. 1, 2002, Retrieved online from https://web.archive.org/web/20021201042357/ http://www.ogscard.com:80/, [retrieved on Sep. 14,… [cited by applicant]
Author Unknown, “Vehicle Identification Number (VIN) OCR,” Klippa. Retrieved online from https://www.klippa.com/en/ocr/data-fields/vins/, [retrieved on Feb. 5, 2025]; 6 pages. [cited by applicant]
Author Unknown “Welcome to the CTA Grading Experts Website,” CTA Grading Experts, Feb. 3, 2006, Retrieved online from https://web.archive.org/web/20060203013241/ http:/ctagradingexperts.com/, [retrieved on Sep. 25, 2023… [cited by applicant]
Author Unknown, “What Is VVS Diamond Clarity and When Should You Choose It?” Jewelry Notes, Jan. 17, 2013, Retrieved online from https://web.archive.org/web/20130117024849/https:/www.jewelrynotes.com/what-is-vvs-diamond… [cited by applicant]
Author Unknown “Your Grading Company for the new Millennium,” CTA Grading Experts, Feb. 13, 2006, Retrieved online from https://web.archive.org/web/20060213014751/http:/www.ctagradingexperts.com/ctaflash.html, [retrieve… [cited by applicant]
Bassett, R. A., “Machine Assisted Grading of Rare Collectibles through the COINS framework”. Dissertation, School of Computer Science and Information Systems, Pace University, Jul. 24, 2003; 194 pages. [cited by applicant]
Bassett, R. A., “Machine assisted visual grading of rare collectibles over the Internet,” Western Connecticut State University, 2003; 12 pages. [cited by applicant]
Berenguel, A., et al., “Evaluation of Texture Descriptors for Validation of Counterfeit Documents,” 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Jan. 29, 2018, pp. 1237-1242. [cited by applicant]
Crisp, S., “How to Grade a Baseball Card: Everything You Ever Wanted To Know,” Ultimate Team Set, Mar. 2, 2015, http://ultimateteamset.com/howtogradeabaseballcard.html, [retrieved online on Sep. 7, 2023], and https://we… [cited by applicant]
Cui, Y., et al., “A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images,” IEEE Access, Jun. 5, 2023, vol. 11, pp. 55297-55315. [cited by applicant]
European Search Report and Written Opinion, EP Application No. 21848631.4, by Collectors Universe, Inc., mailed Jul. 8, 2024; 12 pages. [cited by applicant]
Extended European Search Report for European Application No. 22757010.8, by Collectors Universe, Inc., mailed Dec. 3, 2024; 10 pages. [cited by applicant]
Feng, V., “An Overview of ResNet and its Variants”. Towards Data Science, Jul. 15, 2017, https://towardsdatascience.com/an-overview-of-resnet-and-its-variants-5281e2f56035; 18 pages. [cited by applicant]
Ghanmi, N., et al., “A New Descriptor for Pattern Matching: Application to Identity Document Verification,” 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), Jun. 25, 2018, pp. 375-380. [cited by applicant]
Halperin, J., “Computer Grading”. CoinGrading.com (1999); https://coingrading.com/compgrade1.html, [retrieved on Jul. 20, 2023]; 2 pages. [cited by applicant]
He, K. et al., “Deep Residual Leaming for Image Recognition”. arXiv:1512.03385, Dec. 10, 2015; 12 pages. [cited by applicant]
Huang, S-Y., et al., “Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging,” Sensors, Sep. 26, 2022, 22(19):7308; 18 pages. [cited by applicant]
International Preliminary Report on Patentability for International Application No. PCT/US2022/017010, by Collectors Universe, Inc., mailed Aug. 31, 2023; 9 pages. [cited by applicant]
International Search Report and Written Opinion for International Application No. PCT/US2022/017010, by Collectors Universe, Inc., mailed Jun. 10, 2022; 10 pages. [cited by applicant]
International Search Report and Written Opinion for PCT Application No. PCT/US2024/049604, by Collectors Universe, Inc., mailed Jan. 20, 2025; 25 pages. [cited by applicant]
Lagerstrom, et al., “Objective Image Based Grading of Opal Gemstones”. The 2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, Nevada, USA, Jul. 12-15, 2010, https://p… [cited by applicant]
Liu, L., et al., “Document image classification: Progress over two decades,” Neurocomputing, [Epub May 4, 2021]; Sep. 17, 2021, vol. 453, pp. 223-240. [cited by applicant]
Müller, M., et al., “Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques,” Metals, May 12, 2020, 10(5): 630, pp. 1-19. [cited by applicant]
Nepal, P., “VGGNet Architecture Explained,” Analytics Vidhya, Published on Jul. 30, 2020. Retrieved online from https://medium.com/analytics-vidhya/vggnet-architecture-explained-e5c7318aa5b6#; 5 pages. [cited by applicant]
Non-Final Office Action for U.S. Appl. No. 17/674,328, by Shalamberidze et al., mailed Mar. 20, 2024; 8 pages. [cited by applicant]
Notice of Allowance for U.S. Appl. No. 17/674,328, by Shalamberidize et al., mailed Aug. 14, 2024; 2 pages. [cited by applicant]
Notice of Allowance for U.S. Appl. No. 17/674,328, by Shalamberidze et al., mailed Jul. 31, 2024; 10 pages. [cited by applicant]
Ruheena, B. F., et al., “Fake Currency Detection using Deep Learning Technique,” International Journal of Engineering Research & Technology (IJERT), RTCSIT—2022 Conference Proceedings, 2022, vol. 10, Issue 12, pp. 85-87. [cited by applicant]
Simonyan, K., et al., “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv:1409.1556, Apr. 10, 2015; 14 pages. [cited by applicant]
Sruthy, R., “A review of Fake Currency Recognition Methods,” International Research Joumal of Engineering and Technology (IRJET), Jul. 2022, vol. 9, Issue 7, pp. 2633-2636. [cited by applicant]
Timmer, J., “Computer algorithm can accurately identify Jackson Pollock paintings,” ARS Technica, published on Feb. 12, 2015 [online]. Retrieved from https://web.archive.org/web/20230602195424/https://arstechnica.com/sc… [cited by applicant]
U.S. Currency Education Program, “$1 Note: Issued 1963-Present,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files… [cited by applicant]
U.S. Currency Education Program, “$10 Note: Issued 1914-1990,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/e… [cited by applicant]
U.S. Currency Education Program, “$10 Note: Issued 1990-2000,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/e… [cited by applicant]
U.S. Currency Education Program, “$10 Note: Issued 2000-2006,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/e… [cited by applicant]
U.S. Currency Education Program, “$10 Note: Issued 2006-Present,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/file… [cited by applicant]
U.S. Currency Education Program, “$100 Note: Issued 1914-1990,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/… [cited by applicant]
U.S. Currency Education Program, “$100 Note: Issued 1990-1996,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/… [cited by applicant]
U.S. Currency Education Program, “$100 Note: Issued 1996-2013,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/… [cited by applicant]
U.S. Currency Education Program, “$100 Note: Issued 2013-Present,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/fil… [cited by applicant]
U.S. Currency Education Program, “$2 Note: Issued 1976-Present,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files… [cited by applicant]
U.S. Currency Education Program, “$20 Note: Issued 1914-1990,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/e… [cited by applicant]
U.S. Currency Education Program, “$20 Note: Issued 1990-1998,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/e… [cited by applicant]
U.S. Currency Education Program, “$20 Note: Issued 1998-2003,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/e… [cited by applicant]
U.S. Currency Education Program, “$20 Note: Issued 2003-Present,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/file… [cited by applicant]
U.S. Currency Education Program, “$5 Note: Issued 1914-1993,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/en… [cited by applicant]
U.S. Currency Education Program, “$5 Note: Issued 1993-2000,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/en… [cited by applicant]
U.S. Currency Education Program, “$5 Note: Issued 2000-2008,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/en… [cited by applicant]
U.S. Currency Education Program, “$5 Note: Issued 2008-Present,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files… [cited by applicant]
U.S. Currency Education Program, “$50 Note: Issued 1914-1990,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/e… [cited by applicant]
U.S. Currency Education Program, “$50 Note: Issued 1990-1997,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/e… [cited by applicant]
U.S. Currency Education Program, “$50 Note: Issued 1997-2004,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/e… [cited by applicant]
U.S. Currency Education Program, “$50 Note: Issued 2004-Present,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/file… [cited by applicant]
U.S. Currency Education Program, “Alexander Hamilton: Founding Father of the United States,” Federal Reserve Board, 2018 [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/fil… [cited by applicant]
U.S. Currency Education Program, “Alexander Hamilton: Founding Father of the United States,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/educational-material… [cited by applicant]
U.S. Currency Education Program, “Carnival Thrills and Dollar Bills: A book about U.S. currency,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/educational-mat… [cited by applicant]
U.S. Currency Education Program, “Cashier Toolkit: A Guide to Identifying Genuine Currency,” Federal Reserve Board, 2021 [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/fil… [cited by applicant]
U.S. Currency Education Program, “Cashier Toolkit: A Guide to Identifying Genuine Currency,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/cashier-toolkit, [re… [cited by applicant]
U.S. Currency Education Program, “Decoding Dollars: The $100—A Brochure on $100 Note Security Features,” Federal Reserve Board, 2021 [online]. Retrieved from https://www.uscurrency.gov/educational-materials/decoding-dol… [cited by applicant]
U.S. Currency Education Program, “Decoding Dollars: The $100,” Federal Reserve Board, 2024 [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/en/decoding-dollars-100-bro… [cited by applicant]
U.S. Currency Education Program, “Decoding Dollars: The $20,” Federal Reserve Board, 2024 [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/en/decoding-dollars-20-broch… [cited by applicant]
U.S. Currency Education Program, “Dollars in Detail: Your Guide to U.S. Currency,” Federal Reserve Board, 2024 [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/en/doll… [cited by applicant]
U.S. Currency Education Program, “Download Materials: Download Free Materials About U.S. Currency in 24 Languages,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.g… [cited by applicant]
U.S. Currency Education Program, “How to Check Your Money,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/en/h… [cited by applicant]
U.S. Currency Education Program, “Know the $20,” Federal Reserve Board, 2017 [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/en/know-the-20-tent-en.pdf, [retrieved on… [cited by applicant]
U.S. Currency Education Program, “Play Money Coloring Sheets,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/e… [cited by applicant]
U.S. Currency Education Program, “Printable Play Money,” Federal Reserve Board, first publication date unknown [online]. Retrieved from: https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/en/pri… [cited by applicant]
U.S. Currency Education Program, “Quick Reference Guide,” Federal Reserve Board, 2024 [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/files/en/quick-reference-guide-en-2022… [cited by applicant]
U.S. Currency Education Program, “Teller Toolkit: A Guide To Identifying Genuine Currency,” Federal Reserve Board, 2021 [online]. Retrieved from: https://www.uscurrency.gov/sites/default/files/downloadable-materials/fil… [cited by applicant]
U.S. Currency Education Program, “Teller Toolkit: A Guide to Identifying Genuine Currency,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/teller-toolkit, [retr… [cited by applicant]
U.S. Currency Education Program, “The Latest in U.S. Currency Design,” Multinote Booklet, Federal Reserve Board, Nov. 2016 [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadable-materials/f… [cited by applicant]
U.S. Currency Education Program, “The Latest in U.S. Currency Design,” Multinote Poster, Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/down… [cited by applicant]
U.S. Currency Education Program, “The New $100 Note: Knows Its Features. Know It's Real,” Brochure and Poster for $100 Note. Federal Reserve Board. Apr. 2010 [online]. Retrieved from https://www.uscurrency.gov/sites/def… [cited by applicant]
U.S. Currency Education Program, “Tips to Spot Easy-to-Detect Counterfeit Notes,” Federal Reserve Board, first publication date unknown [online]. Retrieved from https://www.uscurrency.gov/sites/default/files/downloadabl… [cited by applicant]
U.S. Department of Homeland Security: United States Secret Service, “Know Your Money,” Apr. 2016 [online]. Retrieved from https://www.secretservice.gov/sites/default/files/reports/2020-12/KnowYourMoney.pdf, [retrieved o… [cited by applicant]
Wikipedia, “Anomaly detection,” published on Sep. 24, 2023. Retrieved from https://web.archive.org/web/20230924040833/https://en.wikipedia.org/wiki/Anomaly_detection, [retrieved on Feb. 5, 2025]; 2 pages. [cited by applicant]
Wikipedia, “Data augmentation,” published on Sep. 18, 2023. Retrieved from https://web.archive.org/web/20230918152312/https://en.wikipedia.org/wiki/Data_augmentation, [retrieved on Feb. 5, 2025]; 1 page. [cited by applicant]
Wikipedia, “Histogram of oriented gradients,” retrieved from https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients, [retrieved on Oct. 11, 2024]; 7 pages. [cited by applicant]
Yang, J., et al., “Generalized Out-of-Distribution Detection: A Survey,” Arxiv.org., Aug. 3, 2022, arXiv:2110.11334v2 [cs.CV]; 22 pages. [cited by applicant]
Notice of Reasons for Refusal for Japanese Application No. 2023-540512 mailed Apr. 30, 2025; 8 pages with English Translation. [cited by applicant]