Fair Isaac Corporation

Helen Geraldine E Rosario

Ashutosh Joshi

Sergei Tolmanov

Shailesh Kumar

Megan Thorsen

Christopher Allan Ralph

Colin Little

Michael S Sossi

Stuart L Crawford

Gary J Sullivan

Victor Miagkikh

Stefan Kuzminski

Yaxin Liu

John Duchnowski

A product space browser (PSB), which comprises a graphical user interface (GUI) that facilitates insight discovery through exploration and analysis of product space graphs generated by applying a product affinity engine to retailer's transaction data in a market basket context, is disclosed.

Product space browser

A system and method for building segmented scorecards for a population is presented. A model of the population is built using a model builder computer, and one or more variables used by the model builder to build the model is stored in a repository. A scorecard is generated for each segment of the population based on the model and using an adaptive random tree computer program. Next, the scorecard for each segment is enhanced using a integer non-linear programming computer program to determine optimal score weights associated with the variables used by the model builder to build the model, and to generate an enhanced segmented scorecard for the population.

Adaptive random trees integer non-linear programming

The invention provides a purchase sequence browser (PuSB), i.e. a graphical user interface (GUI) that facilitates insight discovery and exploration of product affinities across time, generated by a purchase sequence analysis of a retailer's transaction data. A purchase sequence browser allows the user to browse the most significant product phrases discovered by an exhaustive search of the product affinities across time; explore the retail grammar to create both forward and backward phrase trees or alternate purchase paths starting from, or ending in, a product; generate consistent purchase sequences given some constraints on the products and their order; and profile the value of a product across time with regard to other products fixed in time.

Purchase sequence browser

Data spiders, provide an automated system that can take a file or file store of historic transaction data and create the best set of variables from that data, where 'best' means highly predictive. Genetic algorithms are used to parameterized transactions to form groups, which are subjected na&#xef;ve Bayes score ranking. Variable groups are generated and ranked accord to the score. Data spiders span the full information available, are uncorrelated with previous methods, and are easily interpretable.

Data spiders

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Austin, TX, United States of America

Helen Geraldine E Rosario