US 12,437,307 B2
Fraud detection via automated handwriting clustering
Sujit Eapen, Plainsboro, NJ (US); and Sonil Trivedi, Jersey City, NJ (US)
Assigned to Morgan Stanley Services Group Inc., New York, NY (US)
Filed by Morgan Stanley Services Group Inc., New York, NY (US)
Filed on Mar. 19, 2024, as Appl. No. 18/609,109.
Application 18/609,109 is a continuation of application No. 17/098,443, filed on Nov. 15, 2020, granted, now 11,961,094.
Prior Publication US 2024/0221004 A1, Jul. 4, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/018 (2023.01); G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06F 21/32 (2013.01); G06F 21/64 (2013.01); G06N 3/04 (2023.01); G06Q 10/10 (2023.01); G06V 30/18 (2022.01); G06V 30/262 (2022.01); G06V 30/32 (2022.01)
CPC G06Q 30/0185 (2013.01) [G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06F 21/32 (2013.01); G06F 21/64 (2013.01); G06N 3/04 (2013.01); G06Q 10/103 (2013.01); G06V 30/18086 (2022.01); G06V 30/274 (2022.01); G06V 30/347 (2022.01); G06V 30/36 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A system for automatically analyzing samples of handwritten text to determine a mismatch between a purported writer and an actual writer, comprising:
a text sample database, storing a plurality of digitized images comprising handwritten text;
one or more processors, and
non-transient memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:
receive a first sample of digitized handwriting and metadata associating the first sample with a first individual who allegedly created the sample and with a second individual who allegedly received the sample from the first individual and entered it into a digital system;
receive a second sample of digitized handwriting and metadata associating the second sample with a third individual who allegedly created the sample and with the second individual, who also allegedly received the sample from the third individual and entered it into the digital system;
automatically perform a series of feature extractions to convert the first sample and the second sample into a first vector and a second vector, respectively, of extracted features, wherein the extracted features comprise at least one of: a histogram of oriented gradients, an energy-entropy comparison, and a Pearson coefficient between sets of extracted waveforms from tiles; and
automatically determine that there is a heightened probability that the first individual and third individual did not create the first and second samples, and rather that the second individual created both samples, based at least in part on pairing regions with same semantic contents or functions for pairwise similarity analysis.