Fair Isaac Corporation

Kuhuro Investments Ag, L.l.c.

Yahoo! Inc.

Hnc Software, Inc.

Kevin L Sitze

Michael A Lazarus

Frederique Vernhes

Gerald R Russell

Ted E Dunning

Larry Peranich

William Robert Caid

Matthias Blume

Kenneth B Brown

Larry S Peranich

A U Mattias Blume

William R Caid

Amir Doron

Bradley D Kindig

Philip Mansiel Pellouchoud

Daniel Baird

Michael J Kogan

Mark Alan Laffoon

Sean Cornell Joshlin

Gerald S Russell

Nathan Lavar Clegg

Brad Kindig

Ameen Hikmat Abed

Sean Robert Sullivan

Robert F Kleemann

A U Matthias Blume

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. Predictions of consumer behavior are made by applying nearest-neighbor analysis to consumer vectors.

Predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching

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. Predictions of consumer behavior are made by applying nearest-neighbor analysis to consumer vectors, thus facilitating the targeting of promotional offers to consumers most likely to respond positively.

An online music purchasing system and method is disclosed, in which a radio service operates on a user's computer and is configured for receiving and playing digital radio representations of media content in near real-time in a radio session. A content server is configured for serving the digital radio representations of media content to the radio service, and a radio sequence server is configured for serving a sequential list of currently-playing and previously-played media content of the radio session to the radio service. An account server is configured for receiving a signal indicative of a user's desire to purchase a file of one of a currently-playing or previously-played media content. A download manager is configured for downloading the file to the user's computer according to the signal indicative of the user's desire to purchase the file.

Music purchasing and playing system and method

A system and a method for managing a database. The system includes a database manager for storing and retrieving data records from a database. In one embodiment, the database includes a database data structure that is divided into a plurality of sections. Each of the sections holds is configured to hold zero or more data records. During a store operation of a data record, a key that is associated with the data record is used to identify one of the sections. The system determines whether the identified section has sufficient space to contain the identified data record. If the section has insufficient space, the system deletes data records according to a ranking function. In one embodiment of the invention, the ranking function is a least recently used algorithm. The system of the present invention does not have any inter-dependency between the data records that are stored within the database. Furthermore, in one embodiment of the invention, the system creates the database using sections that are the same size that is used by a caching system to store and retrieve pages from mass storage.

System and method for managing a database

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. Consumer vectors are developed within the vector space, to represent interests of particular consumers by virtue of relative vector positions of consumer and merchant vectors. Various techniques, including clustering, supervised segmentation, and nearest-neighbor analysis, are applied separately or in combination to generate improved predictions of consumer behavior.

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.

Predictive modeling of consumer financial behavior

Growing community of inventors

San Diego, CA, United States of America

Kevin L Sitze