Google Inc.

P Pandurang Nayak

David Robert Bailey

Alexis J Battle

Benedict A Gomes

David Ariel Cohn

Barbara Engelhardt

Bay-Wei W Chang

Kedar Dhamdhere

Christopher Rohrs

Michael Alex Frumkin

Thomas Richard Strohmann

Avni Shah

Robert Spalek

Juraj Gottweis

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

A computer implemented method and system for spell correcting terms within a string of terms that a computer system receives from a computer readable data string representative of a user search query.

Spell correcting long queries

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collecting query term substitution data based on one or more identified concepts. According to one implementation, a method includes receiving a query that includes at least three sequential query terms; determining that the sequential query terms represent a concept; and in response to determining that the sequential query terms represent a concept, collecting query term substitution data for one or more query terms that occur in queries that include the concept.

Using concepts as contexts for query term substitutions

A method of adding web-based content to a web notebook can include producing multiple search results responsive to a search request; generating in the multiple search results multiple corresponding icons, each of which, when invoked, causes content associated with a corresponding search result in the multiple search results to be placed in a web notebook associated with a user account; and forming a correlation between one of the multiple search results and the web notebook.

Managing and accessing data in web notebooks

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

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.

Growing community of inventors

Palo Alto, CA, United States of America

P Pandurang Nayak