Metrics monitoring and financial validation system (M2FVS) for tracking performance of capital, operations, and maintenance investments to an infrastructure

US8725665B2 · kind B2 · utility

Assignee

• The Trustees of Columbia University in the City of New York · US

Inventors

• Roger N. Anderson · New York, US
• Albert Boulanger · New York, US
• Leon L. Wu · New York, US
• Serena Lee · New York, US

Key dates

• Filing date: Aug 20, 2012
• Grant date: May 13, 2014
• Expiry date: Aug 20, 2032

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q10/04
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

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.