Google Inc.

Mario Tanev

Yew Jin Lim

Nir Andelman

William Zhang

Noah B Coccaro

David Faden

Adi Avidor

Lauren Koepnick

Ivan Davtchev

Sagar Gandhi

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that identify and issue search queries expected to be issued in the future. A set of search queries that have been issued by multiple user devices can be obtained. For each query instance, contextual data can be obtained. A first query and its contextual data can be input to a model that outputs the query's likelihood of being issued in the future. The model can be trained using contextual data for training queries and a corresponding labels for the training queries. The learning model outputs the first query's likelihood of being issued in future, and this query is stored as a repeatable query if the likelihood satisfying a repeatability threshold. Subsequently, a stored repeatable query is issued upon a selection of a user selectable interface component and the search engine provides search results for the query.

Identification and issuance of repeatable queries

Methods, systems, and apparatus, including computer program products, in which input keystroke data can be interpreted using a current mapping and a determination can be made whether the current mapping is valid based upon the characters identified by the mapping and the keystroke data. Invalid mappings can be corrected by identifying a transliteration of the characters in an alternative language.

Automatic correction of user input based on dictionary

Methods, systems, and apparatus, including computer program products, in which input keystroke data can be interpreted by a current mapping and a determination can be made whether the current mapping is valid based upon the characters identified by the mapping and the keystroke data. Invalid mappings can be corrected based upon alternative mapping of the keystroke data.

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San Francisco, CA, United States of America

Mario Tanev