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.

Date of Patent:
Mar. 27, 2012

Filed:

Jan. 09, 2009
Applicants:

Xiaoxin Yin, Bothell, WA (US);

Vijay Ravindran Nair, Redmond, WA (US);

Ryan Frederick Stewart, Snoqualmie, WA (US);

Fang Liu, Bellevue, WA (US);

Junhua Wang, Sammamish, WA (US);

Tiffany Kumi Dohzen, Seattle, WA (US);

Yi-min Wang, Bellevue, WA (US);

Inventors:

Xiaoxin Yin, Bothell, WA (US);

Vijay Ravindran Nair, Redmond, WA (US);

Ryan Frederick Stewart, Snoqualmie, WA (US);

Fang Liu, Bellevue, WA (US);

Junhua Wang, Sammamish, WA (US);

Tiffany Kumi Dohzen, Seattle, WA (US);

Yi-Min Wang, Bellevue, WA (US);

Assignee:

Microsoft Corporation, Redmond, WA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 7/00 (2006.01);
U.S. Cl.
CPC ...
Abstract

Systems and methodologies for improved query classification and processing are provided herein. As described herein, a query prediction model can be constructed from a set of training data (e.g., diagnostic data obtained from an automatic diagnostic system and/or other suitable data) using a machine learning-based technique. Subsequently upon receiving a query, a set of features corresponding to the query, such as the length and/or frequency of the query, unigram probabilities of respective words and/or groups of words in the query, presence of pre-designated words or phrases in the query, or the like, can be generated. The generated features can then be analyzed in combination with the query prediction model to classify the query by predicting whether the query is aimed at a head Uniform Resource Locator (URL) or a tail URL. Based on this prediction, an appropriate index or combination of indexes can be assigned to answer the query.


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