Machine learning system for analyzing and establishing tagging trends based on convergence criteria

US7716150B2 · kind B2 · utility

Assignee

• Microsoft Corporation · US

Inventors

• Lili Cheng · Bellevue, US
• Gary W. Flake · Bellevue, US
• Alexander G. Gounares · Kirkland, US
• James R. Larus · Mercer Island, US
• Matthew Bret MacLaurin · Woodinville, US
• Raymond E. Ozzie · Seattle, US
• Thomas F. Bergstraesser · Kirkland, US
• Arnold Blinn · Clyde Hill, US
• Christopher W. Brumme · Mercer Island, US
• Michael Connolly · Royal Oak, US
• Daniel S. Glasser · Mercer Island, US
• Henricus Johannes Maria Meijer · Mercer Island, US
• Debi P. Mishra · Bellevue, US
• Melora Zaner-Godsey · Redmond, US

Key dates

• Filing date: Sep 28, 2006
• Grant date: May 11, 2010
• Expiry date: Dec 3, 2027

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/10
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Systems and methods that analyze aggregated tagging behavior of users, and evaluate such tagging trends to identify criteria for taxonomy applications. Initially, existence of a possible trend of tagging data based on collective user behavior is determined. Subsequently, tagging trends can be examined to identify that a predetermined convergence criteria has in fact been met, and/or establish such criteria for taxonomy applications. Machine learning systems (implicitly as well as explicitly trained) can be supplied to facilitate determining the trends and the convergence criteria.