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:
Jun. 10, 2014
Filed:
Jan. 15, 2013
The Trustees of Columbia University IN the City of New York, New York, NY (US);
Consolidated Edison Company of New York, New York, NY (US);
Roger N. Anderson, New York, NY (US);
Albert Boulanger, New York, NY (US);
Cynthia Rudin, New York, NY (US);
David Waltz, Princeton, NJ (US);
Ansaf Salleb-Aouissi, Middle Village, NY (US);
Maggie Chow, Hartsdale, NY (US);
Haimonti Dutta, Trenton, NJ (US);
Phil Gross, Brooklyn, NY (US);
Huang Bert, Silver Spring, MD (US);
Steve Ierome, New York, NY (US);
Delfina Isaac, New York, NY (US);
Arthur Kressner, Westfiled, NJ (US);
Rebecca J. Passonneau, New York, NY (US);
Axinia Radeva, New York, NY (US);
Leon L. Wu, New York, NY (US);
Peter Hofmann, Hasbrouck Heights, NJ (US);
Frank Dougherty, Yorktown Heights, NY (US);
The Trustees of Columbia University in the city of New York, New York, NY (US);
Consolidated Edison Company of New York, New York, NY (US);
Abstract
A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid.