	US RE42,663 E1
	Predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching
Michael Lazarus, Del Mar, Calif. (US); Larry S. Peranich, San Diego, Calif. (US); Frederique Vernhes, Encinitas, Calif. (US); A. U. Matthias Blume, San Diego, Calif. (US); William R. Caid, San Diego, Calif. (US); Ted E. Dunning, San Diego, Calif. (US); Gerald R. Russell, San Diego, Calif. (US); and Kevin Sitze, San Diego, Calif. (US)
Assigned to Kuhuro Investments AG, L.L.C., Dover, Del. (US)
Filed on Mar. 22, 2010, as Appl. No. 12/729,218.
Application 11/012812 is a division of application No. 09/679022, filed on Oct. 03, 2000, granted, now 6,839,682.
Application 09/679022 is a continuation in part of application No. 09/306237, filed on May 06, 1999, granted, now 6,430,539.
Application 12/729218 is a reissue of application No. 11/012812, filed on Dec. 14, 2004, now 7,165,037, filed on Jan. 16, 2007.
Int. Cl. G06Q 10/00 (2006.01)

U.S. Cl. 705—10

24 Claims

1. A computer implemented method of predicting financial behavior of a target consumer with respect to an offer or merchant, comprising:

for a reference set of consumers, obtaining consumer vectors and data describing financial behavior;

obtaining a consumer vector for the target consumer;

identifying at least one nearest neighbor to the target consumer vector among the reference set of consumers; and

generating a financial behavior prediction for the target consumer by aggregating the financial behavior data of the consumers corresponding to the identified consumer vectors;

wherein generating a behavior prediction comprises:

training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors;

using an unexpected deviation learning approach to determine values of the merchant vectors;

wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and

applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and

wherein identifying at least one nearest neighbor comprises identifying consumer vectors having a dot product between the consumer vector and the target consumer vector that exceeds a predetermined threshold.