Mikhail N Barrett from 11 Murray Ave, Port Washington, NY 11050, age 41

Systems And Methods For Providing Personalized Recommendations For Electronic Content
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US Patent:

20140164401, Jun 12, 2014
Filed:

Dec 10, 2012
Appl. No.:

13/710293
Inventors:

Thu KYAW - Reston VA, US
Sang Chul SONG - Aldie VA, US
Sean Christopher TIMM - Herndon VA, US
Sudhir ACHUTHAN - Vienna VA, US
Mikhail BARRETT - Fairfax VA, US
Vineet MAHAJAN - Ashburn VA, US
International Classification:

G06F 17/30
US Classification:

707751
Abstract:

Computer-implemented systems and methods are provided for presenting content recommendations. In one implementation, a method is provided for providing content recommendations to a plurality of users. The method may include generating a first plurality of content recommendations for a first user using a first plurality of methods, each of the first plurality of methods having a weight associated therewith. The method may further include selecting a second plurality of content recommendations from the first plurality of content recommendations based on the weights associated with the first plurality of methods. The method may further include providing the second plurality of content recommendations to the first user. The method may further include updating the weights associated with the first plurality of methods based on engagement by the first user with the second plurality of content recommendations.

Methods And Systems For Identifying Local Search Queries
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US Patent:

20140095532, Apr 3, 2014
Filed:

Oct 2, 2012
Appl. No.:

13/633488
Inventors:

Sean Christopher TIMM - Herndon VA, US
Sudhir ACHUTHAN - Vienna VA, US
Mikhail Nicholaos BARRETT - Fairfax VA, US
International Classification:

G06F 17/30
US Classification:

707769, 707E17014
Abstract:

Methods and systems are provided for determining whether a search query with an observed number of occurrences in a set of search queries is a local search query. In accordance with one implementation, a method is provided that comprises determining an expected number of occurrences of a search query and comparing the expected number of occurrences to a threshold. Further, the method includes determining whether the search query is a local search query based, at least in part, on the comparison.