David Nathan Cohn

US Patent:

7870147, Jan 11, 2011

Filed:

Nov 22, 2005

Appl. No.:

11/285814

Inventors:

David R. Bailey - Palo Alto CA, US
Alexis J. Battle - Redwood City CA, US
David Ariel Cohn - Palo Alto CA, US
Barbara Engelhardt - San Francisco CA, US
P. Pandurang Nayak - Palo Alto CA, US

Assignee:

Google Inc. - Mountain View CA

International Classification:

G06F 7/00
G06F 17/30

US Classification:

707767, 707769

Abstract:

An information retrieval system includes a query revision architecture providing one or more query revisers, each of which implements a query revision strategy. A query rank reviser suggests known highly-ranked queries as revisions to a first query by initially assigning a rank to all queries, and identifying a set of known highly-ranked queries (KHRQ). Queries with a strong probability of being revised to a KHRQ are identified as nearby queries (NQ). Alternative queries that are KHRQs are provided as candidate revisions for a given query. For alternative queries that are NQs, the corresponding known highly-ranked queries are provided as candidate revisions.

US Patent:

8375049, Feb 12, 2013

Filed:

Sep 7, 2010

Appl. No.:

12/876896

Inventors:

David R. Bailey - Palo Alto CA, US
Alexis J. Battle - Palo Alto CA, US
David Ariel Cohn - Palo Alto CA, US
Barbara Engelhardt - San Francisco CA, US
P. Pandurang Nayak - Palo Alto CA, US

Assignee:

Google Inc. - Mountain View CA

International Classification:

G06F 17/30

US Classification:

707767

Abstract:

An information retrieval system includes a query revision architecture providing one or more query revisers, each of which implements a query revision strategy. A query rank reviser suggests known highly-ranked queries as revisions to a first query by initially assigning a rank to all queries, and identifying a set of known highly-ranked queries (KHRQ). Queries with a strong probability of being revised to a KHRQ are identified as nearby queries (NQ). Alternative queries that are KHRQs are provided as candidate revisions for a given query. For alternative queries that are NQs, the corresponding known highly-ranked queries are provided as candidate revisions.

US Patent:

20160098488, Apr 7, 2016

Filed:

Dec 11, 2015

Appl. No.:

14/966545

Inventors:

- Mountain View CA, US
David Ariel Cohn - Palo Alto CA, US
Carrie Elizabeth Grimes - Palo Alto CA, US
John Ogden Lamping - Los Altos CA, US

International Classification:

G06F 17/30

Abstract:

Systems and methods for deriving and using an interaction profile are described. In one described method, a plurality of metrics indicating a level of satisfaction for search results is determined. The metrics comprise at least one of click-duration data, multiple-click data, and query-refinement data. The values of the metrics for a plurality of instances of an object, such as search results from a search engine, are determined. An interaction profile for the object, based at least in part on the values of the metrics for a plurality of instances of the first object, is then determined. This interaction profile may be used in a variety of ways, such as determining the quality of ranking algorithms and detecting undesirable search results.

US Patent:

20150161255, Jun 11, 2015

Filed:

Dec 1, 2011

Appl. No.:

13/308770

Inventors:

Alexis Jane Battle - Palo Alto CA, US
David Ariel Cohn - Palo Alto CA, US
Carrie Elizabeth Grimes - Palo Alto CA, US
John Ogden Lamping - Los Altos CA, US

Assignee:

GOOGLE INC. - Mountain View CA

International Classification:

G06F 17/30
H04L 29/08

Abstract:

Systems and methods for deriving and using an interaction profile are described. In one described method, a plurality of metrics indicating a level of satisfaction for search results is determined. The metrics comprise at least one of click-duration data, multiple-click data, and query-refinement data. The values of the metrics for a plurality of instances of an object, such as search results from a search engine, are determined. An interaction profile for the object, based at least in part on the values of the metrics for a plurality of instances of the first object, is then determined. This interaction profile may be used in a variety of ways, such as determining the quality of ranking algorithms and detecting undesirable search results.