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.

Date of Patent:
Jul. 21, 2020

Filed:

Mar. 31, 2016
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Ajay A. Deshpande, White Plains, NY (US);

Saurabh Gupta, Irving, TX (US);

Arun Hampapur, Norwalk, CT (US);

Ali Koc, White Plains, NY (US);

Dingding Lin, Beijing, CN;

Xuan Liu, Yorktown Heights, NY (US);

Brian L. Quanz, Yorktown Heights, NY (US);

Yue Tong, Beijing, CN;

Dahai Xing, White Plains, NY (US);

Xiaobo Zheng, Shanghai, CN;

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06Q 10/08 (2012.01); G06Q 30/02 (2012.01); G06N 20/00 (2019.01); G06F 16/14 (2019.01); G06F 16/182 (2019.01); G06F 16/17 (2019.01); G06F 16/23 (2019.01); G06N 5/00 (2006.01); G06F 3/0482 (2013.01); G06F 3/0484 (2013.01); G06Q 30/06 (2012.01); H04L 12/26 (2006.01); G06N 5/04 (2006.01); G06Q 10/06 (2012.01);
U.S. Cl.
CPC ...
G06Q 10/08345 (2013.01); G06F 3/0482 (2013.01); G06F 3/04847 (2013.01); G06F 16/148 (2019.01); G06F 16/1734 (2019.01); G06F 16/183 (2019.01); G06F 16/1844 (2019.01); G06F 16/2365 (2019.01); G06N 5/003 (2013.01); G06N 5/04 (2013.01); G06N 5/045 (2013.01); G06N 20/00 (2019.01); G06Q 10/0633 (2013.01); G06Q 10/06315 (2013.01); G06Q 10/06375 (2013.01); G06Q 10/083 (2013.01); G06Q 10/087 (2013.01); G06Q 10/0833 (2013.01); G06Q 10/0838 (2013.01); G06Q 10/0875 (2013.01); G06Q 30/0201 (2013.01); G06Q 30/0206 (2013.01); G06Q 30/0283 (2013.01); G06Q 30/0284 (2013.01); G06Q 30/0635 (2013.01); H04L 43/0882 (2013.01); H04L 43/16 (2013.01); H04L 43/0876 (2013.01);
Abstract

A historical scenario and historical decisions made in the historical scenario are received. The historical decisions represent a set of decision variables of an objective function. A random set of decision variables having different values than the set of decision variables are generated. To determine a weight setting associated with multiple objectives of the objective function, a number of inequalities are built and solved with an assumption that, for an optimization that minimizes the objective function, the objective function having the set of random decision variables has a larger value than the objective function having the set of decision variables. The receiving, the generating and the building steps may be repeated to determine multiple sets of weight settings. The multiple sets of weight settings are searched to select a target weight setting for each of the multiple objectives. The target weight setting may be automatically and continuously learned.


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