Maggie M Chow, 5262 Eastbrook Ter, Livingston, NJ 07039

2010030, Dec 2, 2010

Jun 1, 2010

12/791363

Maggie Chow - Hartsdale NY, US

CONSOLIDATED EDISON COMPANY - New York NY

G06Q 10/00
G06Q 50/00
G06F 15/18

705 8, 705 7, 706 12

A computer-implemented method and system performing allocating capital assets for managing a plurality of utility service components. The method includes ranking each of the utility service components based on data retrieved corresponding to the utility service components, calculating a base failure metric for each of the utility service components, receiving a selection of at least one utility service component of the plurality of utility service components inputted by a user, analyzing the selected utility service component under a plurality of improvement scenarios, calculating an estimated failure metric of the selected utility service component based on each of the improvement scenarios, and displaying comparison information between the base failure metric and the estimated failure metric.

2011028, Nov 17, 2011

May 11, 2010

12/777803

Maggie Chow - Hartsdale NY, US
Mark Mastrocinque - East Northport NY, US
Robert J. Blick - Bellerose NY, US

CONSOLIDATED EDISON COMPANY OF NEW YORK, INC. - New York NY

G06Q 10/00

705 711

A system and computer-implemented method of providing contingency analysis information for a utility service network that includes obtaining contingency analysis information from a plurality of external sources, integrally combining the contingency analysis information obtained from each of the plurality of external sources into a single application and prioritizing the contingency analysis information in a predetermined order, dynamically updating, the contingency analysis information obtained from each of the plurality of external sources and the prioritization of the contingency analysis information based on status information, and displaying the contingency analysis information to a user via a graphical user interface.

2013013, May 30, 2013

May 23, 2012

13/479198

Roger N. Anderson - New York NY, US
Maggie Chow - Hartsdale NY, US
Albert Boulanger - New York NY, US

G06Q 10/06

705 737

A capital asset planning system for selecting assets for improvement within an infrastructure that includes one or more data sources descriptive of the infrastructure, one or more databases, coupled to the one or more data sources, to compile the one or more data sources, one or more processors, each coupled to and having respective communication interfaces to receive data from the one or more databases. The processor includes a predictor to generate a first metric of estimated infrastructure effectiveness based, at least in part, on a current status of the infrastructure, a second metric of estimated infrastructure effectiveness based, at least in part, on a user-selected, proposed changed configuration of the infrastructure, and a net metric of infrastructure effectiveness based, at least in part, on said first metric and said second metric. The system also includes a display, coupled to have the one or more processors, for visually presenting the net metric of infrastructure effectiveness, in which the assets for improvement are selected based, at least in part, on the net metric of infrastructure effectiveness.

2013023, Sep 5, 2013

Jan 15, 2013

13/742124

Cynthia Rudin - New York NY, US
David Waltz - Princeton NJ, 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

Consolidated Edison Company of New York - New York NY
The Trustees of Columbia University in the City of New York - New York NY

G06N 99/00

706 12

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.