Arijit Ghosh, 4743 Palisades Rd, Old Bridge, NJ 08857

7822631, Oct 26, 2010

Nov 30, 2005

11/290623

Christopher D. Vander Mey - Seattle WA, US
Arijit Ghosh - Mercer Island WA, US
Brian David Marsh - Seattle WA, US

Amazon Technologies, Inc. - Incline Village NV

G06F 17/30

705 10

Techniques are described for managing content by identifying content that has attributes of interest (e. g. , content that is useful, humorous and/or that otherwise has a sufficiently high degree of quality) and by determining how to use such identified content. In some situations, a piece of content is automatically assessed in a manner based on automatically assessed levels of trust in users who are associated with the content, such as a user who authored or otherwise supplied the content and/or users who evaluated the content. For example, an automatically assessed level of trust for a user may be based on prior activities of the user and be used to predict future behavior of the user as a supplier of acceptable content and/or as an acceptable evaluator of supplied content, such as based on prior activities of the user that are not related to supplying and/or evaluating content.

7519562, Apr 14, 2009

Mar 31, 2005

11/096699

Christopher D. Vander Mey - Seattle WA, US
Arijit Ghosh - Mercer Island WA, US
Nikolas C. Gloy - Bellevue WA, US

Amazon Technologies, Inc. - Incline Village NV

G06Q 90/00
G06Q 30/00

705500, 705 12, 705 26

Techniques are described for enhancing the reliability of information received from users in various ways, such as by analyzing votes or other ratings supplied by a user in order to identify fraudulent and other unreliable ratings. Such analysis may be based at least in part on prior ratings submitted by the user, such as based on one or more patterns indicating that the user has a bias for or against one or more of various aspects. Unreliable ratings can then be excluded from use in various ways. This abstract is provided to comply with the rules requiring it, and is submitted with the intention that it not limit the scope of the claims.

2015006, Mar 5, 2015

Jan 26, 2012

13/359073

Arijit Ghosh - Redmond WA, US
Umesh Unnikrishnan - Seattle WA, US

G06Q 30/02

705 1443

Methods, systems, and non-transitory computer-readable media for evaluating changes to one or more advertising campaigns of an advertiser. In one aspect, advertising campaign modification data, specifying advertising campaign changes, is received from a campaign modification data source. A queue of changes based on the advertising campaign modification data is generated, and the changes from the queue are evaluated. It is determined whether to apply the changes to the advertising campaign based on results of the evaluating step and rule data specifying advertising campaign performance metrics and predefined performance metric values.