Oracle International Corporation

Profitlogic, Inc.

Peter Gaidarev

Jonathan W Woo

Mahesh Kumar

Su-Ming Wu

Z Maria Wang

Sandeep Tiwari

A system that predicts promotional cross item ('PCI') effects for retail items for a store receives historical sales data for the store and stores the historical sales data in a panel data format. The system then aggregates the stored sales data as a first level of aggregation that is aggregated to the store, a product and a time period. The system further aggregates the first level of aggregation aggregated data as a second level of aggregation that is based on a promotional cross effect attribute ('PCEA') and is aggregated to the store, the time period and a PCEA level. The system derives PCI effect predictor variables from the second level of aggregation and, for each PCEA within a retail item family, forms a regression model. The system then generates estimated model parameters for one or more PCI effects for each PCEA from the regression models.

Retail sales forecast system with promotional cross-item effects prediction

A system generates a consumer decision tree ('CDT'). The system receives customer purchasing data that includes transactions of a plurality of products each having at least one product attribute. For a product category, the system identifies a plurality of similar products from the purchasing data and one or more attributes corresponding to each similar product. The system assigns the product category as a current level of the CDT, and determines a most significant attribute of the plurality of attributes for the current level. The system forms a next level of the CDT by dividing the most significant attribute into a plurality of sub-sections, where each sub-section corresponds to an attribute value of the most significant attribute. The system then forms a next level of the CDT for each sub-section until a terminal node is identified.

Consumer decision tree generation system

Based on a metric that represents a value of a game of chance, a payout distribution is optimized with respect to the metric.

Payout distributions for games of chance

A set of data is received containing values associated with respective data points, the values associated with each of the data points being characterized by a distribution. The values for each of the data points are expressed in a form that includes information about a distribution of the values for each of the data points. The distribution information is used in clustering the set of data with at least one other set of data containing values associated with data points.

Methods for estimating the seasonality of groups of similar items of commerce data sets based on historical sales date values and associated error information

Methods for estimating the seasonality of groups of similar items of commerce data sets based on historical sales data values and associated error information

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Malden, MA, United States of America

Peter Gaidarev