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:
Aug. 04, 2020

Filed:

Jun. 08, 2017
Applicant:

Bigwood Technology, Inc., Ithaca, NY (US);

Inventors:

Hsiao-Dong Chiang, Ithaca, NY (US);

Shuo Wang, Tianjin, CN;

Assignee:

Bigwood Technology, Inc., Ithaca, NY (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 30/00 (2020.01); G05B 13/02 (2006.01); G06F 17/11 (2006.01); G06Q 10/04 (2012.01); G05B 13/04 (2006.01); G06F 30/20 (2020.01); G06F 111/06 (2020.01); G06F 1/00 (2006.01); H04L 29/08 (2006.01);
U.S. Cl.
CPC ...
G06F 30/00 (2020.01); G05B 13/024 (2013.01); G05B 13/042 (2013.01); G06F 1/00 (2013.01); G06F 17/11 (2013.01); G06F 30/20 (2020.01); G06Q 10/04 (2013.01); H04L 67/306 (2013.01); G06F 2111/06 (2020.01);
Abstract

A user-preference-enabling (UPE) method optimizes operations of a system based on user preferences. The operations of the system are modeled as a user-preference-based multi-objective optimization (MOO) problem having multiple object functions subject to a set of constraints. The set of constraints include system constraints and a wish list specifying a respective user-preferred range of values for one or more of the objective functions. The UPE method calculates a wish list feasible solution (WL-feasible solution) to the user-preference-based MOO problem. The UPE method can be performed iteratively to compute targeted Pareto-optimal solutions. The UPE method can be used in a hybrid method in combination with other numerical methods to reliably compute feasible solutions of both conventional MOO problems and user-preference-based MOO problems.


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