KENNETH S. BROWN, M.D.
Anesthesiologist Assistant at Ruffin Rd, San Diego, CA

6430539, Aug 6, 2002

May 6, 1999

09/306237

Michael A. Lazarus - Del Mar CA
A. U. Mattias Blume - San Diego CA
Kenneth B. Brown - San Diego CA
William R. Caid - San Diego CA
Ted E. Dunning - San Diego CA
Larry S. Peranich - San Diego CA
Gerald R. Russell - San Diego CA
Kevin L. Sitze - San Diego CA

HNC Software - San Diego CA

G06F 1760

705 10, 706 6, 705 14, 705 26

Predictive modeling of consumer financial behavior is provided by application of consumer transaction data to predictive models associated with merchant segments. Merchant segments are derived from consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors representing specific merchants are clustered to form merchant segments in a vector space as a function of the degree to which merchants co-occur more or less frequently than expected. Each merchant segment is trained using consumer transaction data in selected past time periods to predict spending in subsequent time periods for a consumer based on previous spending by the consumer. Consumer profiles describe summary statistics of consumer spending in and across merchant segments. Analysis of consumers associated with a segment identifies selected consumers according to predicted spending in the segment or other criteria, and the targeting of promotional offers specific to the segment and its merchants.

7533038, May 12, 2009

Jan 15, 2007

11/623266

Matthias Blume - San Diego CA, US
Michael A. Lazarus - San Diego CA, US
Larry S. Peranich - San Diego CA, US
Frederique Vernhes - Encinitas CA, US
Kenneth B. Brown - San Diego CA, US
William R. Caid - San Diego CA, US
Ted E. Dunning - San Diego CA, US
Gerald R. Russell - San Diego CA, US
Kevin L. Sitze - San Diego CA, US

Fair Isaac Corporation - Minneapolis MN

G06Q 10/00

705 10

Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Supervised segmentation is applied to merchant vectors to form the merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments. The consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer.

6134532, Oct 17, 2000

Nov 14, 1997

8/971091

Michael A. Lazarus - Del Mar CA
William R. Caid - San Diego CA
Richard S. Pugh - Poway CA
Bradley D. Kindig - Poway CA
Gerald S. Russell - San Diego CA
Kenneth B. Brown - San Diego CA
Ted E. Dunning - San Diego CA
Joel L. Carleton - San Diego CA

Aptex Software, Inc. - San Diego CA

G06F 1760

705 14

A system and method for selecting and presenting personally targeted entities such as advertising, coupons, products and information content, based on tracking observed behavior on a user-by-user basis and utilizing an adaptive vector space representation for both information and behavior. The system matches users to entities in a manner that improves with increased operation and observation of user behavior. User behavior and entities (ads, coupons, products) and information (text) are all represented as content vectors in a unified vector space. The system is based on an information representation called content vectors that utilizes a constrained self organization learning technique to learn the relationships between symbols (typically words in unstructured text). Users and entities are each represented as content vectors.

7165037, Jan 16, 2007

Dec 14, 2004

11/012812

Michael A. Lazarus - Del Mar CA, US
Larry S. Peranich - San Diego CA, US
Frederique Vernhes - Encinitas CA, US
A. U. Mattias Blume - San Diego CA, US
Kenneth B. Brown - San Diego CA, US
William R. Caid - San Diego CA, US
Ted E. Dunning - San Diego CA, US
Gerald R. Russell - San Diego CA, US
Kevin L. Sitze - San Diego CA, US

Fair Isaac Corporation - San Diego CA

G06Q 10/00

705 10

Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments, which are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur. Supervised segmentation is applied to merchant vectors to form merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments. Consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer.