Roger E Anderson

US Patent:

6826483, Nov 30, 2004

Filed:

Oct 13, 2000

Appl. No.:

09/687813

Inventors:

Roger N. Anderson - Palisades NY
Albert Boulanger - Palisades NY
Wei He - Palisades NY
Jody Winston - Palisades NY
Liquing Xu - Palisades NY
Ulisses Mello - Yorktown Heights NY
Wendell Wiggins - Houston TX

Assignee:

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

International Classification:

G01V 128

US Classification:

702 13, 702 14

Abstract:

A single intranet, internet, or World Wide Web-accessible interface is provided for, initiation of, interactive adjustments to, and access to the outputs of an integrated workflow of a plurality of analytical computer applications for characterization and analysis of traits and optimal management of the extraction of oil, gas, and water from a subsurface reservoir. By combining disparate analytical application tools in a seamless and remotely accessible, package, incompatibility problems caused by the disparate nature of petroleum analysis methods is reduced. The assumptions, analytic processes, and input data used for one analysis may be readily retrieved and re-evaluated for that reservoir or for future evaluations of the same or other reservoirs. Thus a flexible database of analysis tools and data may be implemented for access, input, and output of workflow and analytical data in the field, in conjunction with standard main computer servers, software and plug-ins, and portable remote computers.

US Patent:

7395252, Jul 1, 2008

Filed:

Feb 8, 2006

Appl. No.:

11/349711

Inventors:

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

Assignee:

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

International Classification:

G06F 17/00
G06N 5/00

US Classification:

706 45, 706 46, 706 52

Abstract:

An Innervated Stochastic Controller optimizes business decision-making under uncertainty through time. The Innervated Stochastic Controller uses a unified reinforcement learning algorithm to treat multiple interconnected operational levels of a business process in a unified manner. The Innervated Stochastic Controller generates actions that are optimized with respect to both financial profitability and engineering efficiency at all levels of the business process. The Innervated Stochastic Controller can be configured to evaluate real options. In one embodiment of the invention, the Innervated Stochastic Controller is configured to generate actions that are martingales. In another embodiment of the invention, the Innervated Stochastic Controller is configured as a computer-based learning system for training power grid operators to respond to grid exigencies.

US Patent:

7945524, May 17, 2011

Filed:

Jul 23, 2008

Appl. No.:

12/178553

Inventors:

Roger N. Anderson - New York NY, US
Albert Boulanger - New York NY, US
David L. Waltz - Princeton NJ, US
Phil Long - Palo Alto CA, US
Marta Arias - Barcelona, ES
Philip Gross - New York NY, US
Hila Becker - Plainview NY, US
Arthur Kressner - New York NY, US
Mark Mastrocinque - East Northport NY, US
Matthew Koenig - Valley Stream NY, US
John A. Johnson - Belle Harbor NY, US

Assignee:

The Trustess of Columbia University in the City of New York - NY NY
Consolidated Edison of New York, Inc. - NY NY

International Classification:

G06F 15/18

US Classification:

706 12

Abstract:

A machine learning system creates failure-susceptibility rankings for feeder cables in a utility's electrical distribution system. The machine learning system employs martingale boosting algorithms and Support Vector Machine (SVM) algorithms to generate a feeder failure prediction model, which is trained on static and dynamic feeder attribute data. Feeders are dynamically ranked by failure susceptibility and the rankings displayed to utility operators and engineers so that they can proactively service the distribution system to prevent local power outages. The feeder rankings may be used to redirect power flows and to prioritize repairs. A feedback loop is established to evaluate the responses of the electrical distribution system to field actions taken to optimize preventive maintenance programs.

US Patent:

8560476, Oct 15, 2013

Filed:

Jan 24, 2008

Appl. No.:

12/019347

Inventors:

Roger N. Anderson - New York NY, US
Albert Boulanger - New York NY, US
Wei He - Tappan NY, US
Ulisses Mello - Yorktown Heights NY, US
Liqing Xu - Tenafly NJ, US

Assignee:

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

International Classification:

G06F 17/00
G06N 5/00
G06G 7/48

US Classification:

706 45, 703 10

Abstract:

A computer-aided lean management (CALM) controller system recommends actions and manages production in an oil and gas reservoir/field as its properties and conditions change with time. The reservoir/field is characterized and represented as an electronic-field (“e-field”). A plurality of system applications describe dynamic and static e-field properties and conditions. The application workflows are integrated and combined in a feedback loop between actions taken in the field and metrics that score the success or failure of those actions. A controller/optimizer operates on the combination of the application workflows to compute production strategies and actions. The controller/optimizer is configured to generate a best action sequence for production, which is economically “always-in-the-money. ”.

US Patent:

20110175750, Jul 21, 2011

Filed:

Sep 20, 2010

Appl. No.:

12/885750

Inventors:

Roger Anderson - New York NY, US
Albert Boulanger - New York NY, US
Philip Gross - New York NY, US
Bob Blick - Bellerose NY, US
Leon Bukhman - Brooklyn NY, US
Mark Mastrocinque - East Northport NY, US
John Johnson - Belle Harbor NY, US
Fred Seibel - Santa Fe NM, US
Hubert Delany - New Rochelle NY, US

Assignee:

The Trustees Of Columbia University In The City Of New York - New York NY
Consolidated Edison, Inc. - New York NY

International Classification:

G08B 21/00

US Classification:

34087016

Abstract:

The disclosed subject matter relates to an integrated decision support “cockpit” or control center for displaying, analyzing, and/or responding to, various events and contingencies that can occur within an electrical grid.

US Patent:

20110231213, Sep 22, 2011

Filed:

Sep 20, 2010

Appl. No.:

12/885800

Inventors:

Roger N. Anderson - New York NY, US
Albert Boulanger - New York NY, US
Samantha Cook - New York NY, US
John Johnson - Belle Harbor NY, US

Assignee:

THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK - New York NY

International Classification:

G06Q 10/00

US Classification:

705 711

Abstract:

The present application provides methods and systems for quantitatively predicting an effectiveness of a proposed capital improvement project based on one or more previous capital improvement projects representative of one or more physical assets and including one or more attributes that includes defining a first sample pool from the previous capital improvement project data in which said previous capital improvement project has been performed, defining a second sample in which the previous capital improvement project has not been performed, the second sample pool including one or more attribute values that are the same as, or similar to, the attribute values for the first sample pool, generating a performance metric for each of the first and second sample pools, comparing the performance metric from the first sample pool with the performance metric from the second sample pool to determine a net performance metric, and, generating a prediction of effectiveness of the proposed capital improvement project concerning based on said net performance metric.

US Patent:

20120072039, Mar 22, 2012

Filed:

Aug 19, 2011

Appl. No.:

13/214057

Inventors:

Roger N. Anderson - New York NY, US
Albert Boulanger - New York NY, US
John A. Johnson - Belle Harbor NY, US

International Classification:

G06F 1/28
G06F 17/00

US Classification:

700291, 700297, 700 90, 713300

Abstract:

The disclosed subject matter provides systems and methods for allocating resources within an infrastructure, such as an electrical grid, in response to changes to inputs and output demands on the infrastructure, such as energy sources and sinks. A disclosed system includes one or more processors, each having respective communication interfaces to receive data from the infrastructure, the data comprising infrastructure network data, one or more software applications, operatively coupled to and at least partially controlling the one or more processors, to process and characterize the infrastructure network data; and a display, coupled to said one or more processors, for visually presenting a depiction of at least a portion of the infrastructure including any changes in condition thereof, and one or more controllers in communication with the one or more processors, to manage processing of the resource, wherein the resource is obtained and/or distributed based on the characterization of said real time infrastructure data.

US Patent:

20130073488, Mar 21, 2013

Filed:

Aug 20, 2012

Appl. No.:

13/589737

Inventors:

Roger N. Anderson - New York NY, US
Albert Boulanger - New York NY, US
Leon L. Wu - New York NY, US

International Classification:

G06F 15/18

US Classification:

706 12

Abstract:

Techniques for evaluating the accuracy of a predicted effectiveness of an improvement to an infrastructure include collecting data, representative of at least one pre-defined metric, from the infrastructure during first and second time periods corresponding to before and after a change has been implemented, respectively. A machine learning system can receive compiled data representative of the first time period and generate corresponding machine learning data. A machine learning results evaluator can empirically analyze the generated machine learning data. An implementer can implement the change to the infrastructure based at least in part on the data from a machine learning data outputer. A system performance improvement evaluator can compare the compiled data representative of the first time period to that of the second time period to determine a difference, if any, and compare the difference, if any, to a prediction based on the generated machine learning data.