Edwin P Pednault

20030176931, Sep 18, 2003

Mar 11, 2002

10/096474

Edwin Pednault - Cortlandt Manor NY, US
Ramesh Natarajan - Pleasantville NY, US

International Business Machines Corporation - Armonk NY

G06F007/60
G06F017/10
G05B013/02

700/031000, 700/030000, 700/044000

The present invention generally relates to computer databases and, more particularly, to data mining and knowledge discovery. The invention specifically relates to a method for constructing segmentation-based predictive models, such as decision-tree classifiers, wherein data records are partitioned into a plurality of segments and separate predictive models are constructed for each segment. The present invention contemplates a computerized method for automatically building segmentation-based predictive models that substantially improves upon the modeling capabilities of decision trees and related technologies, and that automatically produces models that are competitive with, if not better than, those produced by data analysts and applied statisticians using traditional, labor-intensive statistical techniques. The invention achieves these properties by performing segmentation and multivariate statistical modeling within each segment simultaneously. Segments are constructed so as to maximize the accuracies of the predictive models within each segment. Simultaneously, the multivariate statistical models within each segment are refined so as to maximize their respective predictive accuracies.

20030208402, Nov 6, 2003

Mar 27, 2003

10/401935

Eric Bibelnieks - Ogema WI, US
Mark Bullock - Racine WI, US
Michael Haydock - Chanhassen MN, US
Mindy Schiller - Tampa FL, US
Wayne Kugel - Chisago City MN, US
Edwin Pednault - Cortlandt Manor NY, US
Nancy Soderquist - Anoka MN, US
Harlan Crowder - Sunnyvale CA, US

G06F017/60

705/014000

A method, and system for increasing the efficiency of customer contact strategies is disclosed. Customers are analyzed based upon historical criteria; a promotional plan (a group of promotion events implemented or to be implemented over a particular time period) is analyzed to determine the effect of each promotion event on the other promotion events in the promotional plan; and, based on this analysis, the optimal promotion stream (a specific subset of the promotional plan to be sent to customers or a group of similar customers) is determined so as to maximize the ROI of the promotional plan as a whole.

20040015386, Jan 22, 2004

Jul 19, 2002

10/198102

Naoki Abe - Rye NY, US
Edwin Pednault - Cortlandt Manor NY, US

International Business Machines Corporation - Armonk NY

G06F017/60

705/010000

A system and method for sequential decision-making for customer relationship management includes providing customer data including stimulus-response history data, and automatically generating actionable rules based on the customer data. Further, automatically generating actionable rules may include estimating a value function using reinforcement learning.

20050125474, Jun 9, 2005

Dec 5, 2003

10/727578

Edwin Peter Pednault - Cortlandt Manor NY, US

International Business Machines Corporation - Armonk NY

G06F017/14

708400000

A predictive model method (and structure) includes receiving an input data into an initial model to develop an initial model output and receiving both of the input data and the initial model output as input data into a first transform/regression stage.

20070159481, Jul 12, 2007

Jan 11, 2006

11/329437

Naoki Abe - Rye NY, US
Edwin Pednault - Cortlandt Manor NY, US
Fateh Tipu - Wappingers Falls NY, US

G06T 11/20

345440000

Feature importance information available in a predictive model with correlation information among the variables is presented to facilitate more flexible choices of actions by business managers. The displayed feature importance information combines feature importance information available in a predictive model with correlational information among the variables. The displayed feature importance information may be presented as a network structure among the variables as a graph, and regression coefficients of the variables indicated on the corresponding nodes in the graph. To generate the display, a regression engine is called on a set of training data that outputs importance measures for the explanatory variables for predicting the target variable. A graphical model structural learning module is called that outputs a graph on the explanatory variables of the above regression problem representing the correlational structure among them. The feature importance measure, output by the regression engine, is displayed for each node in the graph, as an attribute, such as color, size, texture, etc, of that node in the graph output by the graphical model structural learning module.

20070174105, Jul 26, 2007

Jan 20, 2006

11/335684

Naoki Abe - Rye NY, US
Edwin Pednault - Cortlandt Manor NY, US

G06F 17/30

705010000

A method of marketing optimization with respect to brand lifetime management formulates a problem of brand equity maximization utilizing Markov Decision Process (MDP) thereby casting brand equity management as a long term regard optimization problem in MDP, The marketing mix is optimized by formulating the mix as actions in MDP and, utilizing historical marketing and transaction data, aspects of the MDP are estimated.

20080015936, Jan 17, 2008

Jul 16, 2007

11/778236

Eric Bibelnieks - Ogema WI, US
Mark Bullock - Racine WI, US
Michael Haydock - Chanhassen MN, US
Mindy Schiller - Tampa FL, US
Wayne Kugel - Chisago City MN, US
Edwin Pednault - Cortlandt Manor NY, US
Nancy Soderquist - Anoka MN, US
Harlan Crowder - Sunnyvale CA, US

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

G06Q 30/00

705014000

A method, and system for increasing the efficiency of customer contact strategies is disclosed. Customers are analyzed based upon historical criteria; a promotional plan (a group of promotion events implemented or to be implemented over a particular time period) is analyzed to determine the effect of each promotion event on the other promotion events in the promotional plan; and, based on this analysis, the optimal promotion stream (a specific subset of the promotional plan to be sent to customers or a group of similar customers) is determined so as to maximize the ROI of the promotional plan as a whole.

20080249844, Oct 9, 2008

Jun 17, 2008

12/140846

Naoki Abe - Rye NY, US
Edwin P.D. Pednault - Cortlandt Manor NY, US

International Business Machines Corporation - Armonk NY

G06Q 50/00

705 10

A system and method for sequential decision-making for customer relationship management includes providing customer data including stimulus-response history data, and automatically generating actionable rules based on the customer data. Further, automatically generating actionable rules may include estimating a value function using reinforcement learning.