The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Apr. 14, 2009
Filed:
May. 02, 2007
Naoki Abe, Rye, NY (US);
James J. Bennett, Fairport, NY (US);
David L. Jensen, Peekskill, NY (US);
Richard D. Lawrence, Ridgefield, CT (US);
Prem Melville, White Plains, NY (US);
Edwin Peter Dawson Pednault, Cortlandt Manor, NY (US);
Cezar Pendus, Yardley, PA (US);
Chandan Karrem Reddy, Ossining, NY (US);
Vincent Philip Thomas, Severna Park, MD (US);
Naoki Abe, Rye, NY (US);
James J. Bennett, Fairport, NY (US);
David L. Jensen, Peekskill, NY (US);
Richard D. Lawrence, Ridgefield, CT (US);
Prem Melville, White Plains, NY (US);
Edwin Peter Dawson Pednault, Cortlandt Manor, NY (US);
Cezar Pendus, Yardley, PA (US);
Chandan Karrem Reddy, Ossining, NY (US);
Vincent Philip Thomas, Severna Park, MD (US);
International Business Machines Corporation, Armonk, NY (US);
Abstract
The present invention employs data processing systems to handle debt collection by formulation the collections process as a Markov Decision Process with constrained resources, thus making it possible automatically to generate an optimal collections policy with respect to maximizing long-term expected return throughout the course of a collections process, subject to constraints on the available resources possibly in multiple organizations. This is accomplished by coupling data modeling and resource optimization within the constrained Markov Decision Process formulation and generating optimized rules based on constrained reinforcement learning process comprising applied on the basis of past historical data.