Fair Isaac Corporation

Fair Issac Corporation

Shailesh Kumar

Edmond D Chow

Michinari Momma

Ashutosh Joshi

Sergei Tolmanov

Megan Thorsen

Helen Geraldine E Rosario

Vesselin Diev

Colin Little

Scott Michael Zoldi

Zhenyu Yan

Stuart L Crawford

Victor Miagkikh

Marc-david Cohen

Junwen Wu

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 computer-implemented method described for facilitating selection of an assortment of products to offer for sale. The method includes receiving transaction data representing characteristics of a plurality of commercial transactions including a first product and a second product. Product attribute data representing attributes for at least the two products are also received. Substitution demand data for the second product is estimated which represents demand for the second product given the first product is not available, the estimation being based on the transaction data and product attribute data.

Assortment planning based on demand transfer between products

A method of modeling includes quantifying a co-operative strength value for a plurality of pairs of variables, and identifying a clique of at least three variables based on a graph of the co-operative strength values of a plurality of pairs of variables. The method also includes selecting a first pair of variables of the plurality of pairs of variables having a high co-operative strength value. A second clique may also be identified. A model of the first clique and a model of the second clique are made. The outputs of these models are combined to form a combined model which is used to make various decisions with respect to real time data.

Co-occurrence consistency analysis method and apparatus for finding predictive variable groups

A system for classifying a transaction as fraudulent includes a training component and a scoring component. The training component acts on historical data and also includes a multi-dimensional risk table component comprising one or more multidimensional risk tables each of which approximates an initial risk value for a substantially empty cell in a risk table based upon risk values in cells related to the substantially empty cell. The scoring component produces a score, based in part, on the risk tables associated with groupings of variables having values determined by the training component. The scoring component includes a statistical model that produces an output and wherein the transaction is classified as fraudulent when the output is above a selected threshold value.

Incremental factorization-based smoothing of sparse multi-dimensional risk tables

The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.

Method and apparatus for recommendation engine using pair-wise co-occurrence consistency

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

Method and apparatus for initiating a transaction based on a bundle-lattice space of feasible product bundles

Method and apparatus for retail data mining using pair-wise co-occurrence consistency

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San Diego, CA, United States of America

Shailesh Kumar