Google Inc.

A9.com, Inc.

Amazon Technologies, Inc.

David Robert Bailey

Alexis J Battle

P Pandurang Nayak

Benedict A Gomes

Amit Singhal

Matthew K Gray

Anand Rajaraman

Nundu Janakiram

Grant Dasher

Todd J Feldman

Udi Manber

Ruben Ernesto Ortega

Krzysztof Duleba

Jonathan James Effrat

David Ariel Cohn

Lukasz Andrzej Heldt

Barbara Engelhardt

Johnathan J Effrat

Barbara Englehardt

Pandurang P Nayak

Systems and methods for approximating a user location are provided. For instance, historical location data and internet protocol address data can be analyzed to identify a plurality of locations. A confidence score for each of the plurality of locations can be determined. Two or more locations of the plurality of locations that form a cluster can be identified and the confidence scores for each of the two or more locations that form a cluster can be modified by adjusting each confidence score by a weight associated with the cluster.

Approximating a user location

An information retrieval system includes a query revision architecture that integrates multiple different query revisers, each implementing one or more query revision strategies. A revision server receives a user's query, and interfaces with the various query revisers, each of which generates one or more potential revised queries. The revision server evaluates the potential revised queries, and selects one or more of them to provide to the user. A session-based reviser suggests one or more revised queries, given a first query, by calculating an expected utility for the revised query. The expected utility is calculated as the product of a frequency of occurrence of the query pair and an increase in quality of the revised query over the first query.

Estimating confidence for query revision models

Methods, systems, and computer program products are provided for interleaving search results. A method includes presenting multiple first search results received from a first search engine. The first search results satisfy a search query directed to the first search engine and are presented in an order. A second search result from a second search engine is inserted at a position between two otherwise adjacent first search results. The second search result is received from a second search engine in response to the search query.

Interleaving search results

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing image search results with web corpus search results. In one aspect, a method includes determining dimensions of an image display environment in which image search results are to be displayed, determining an insertion score that defines an ordinal insertion position at which the image display environment is to be inserted into a ranking of general search results that defines ordinal positions for each of the general search results, the dimensions and the insertion score determined, in part, from the quality scores of the images, generating a search results resource for displaying the general search results according to their respective ordinal positions and the image display environment at the ordinal insertion position on a client device, and providing the search results resource in response to the search query.

Dynamic image display area and image display within web search results

Systems and methods are described for identifying item attributes that are likely to be useful to users in refining searches for items. In one embodiment, when a user submits a search query to search for items, all or a portion of the search query is used to look-up a previously-generated list of item attributes (and optionally associated attribute values) that are associated with the search query and are useful for refining the search. These item attributes, and any associated attribute values, are then presented to the user in a selectable form such that the user can interactively narrow the search.

Predictive selection of item attributes likely to be useful in refining a search

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.

Query revision using known highly-ranked queries

Computer-implemented methods are described for identifying item attributes that are likely to be useful to users in refining searches for items. In one embodiment, when a user submits a search query to search for items, all or a portion of the search query is used to look-up a previously-generated list of item attributes (and optionally associated attribute values) that, based on a programmatic analysis that includes an analysis of user activity of a population of users, are associated with the search query and are useful for refining the search. These item attributes, and any associated attribute values, are then presented to the user in a selectable form such that the user can interactively narrow the search.

Growing community of inventors

Palo Alto, CA, United States of America

David Robert Bailey