Google Inc.

Alessandro Agostini

Behshad Behzadi

Marcin M Nowak-Przygodzki

Evgeny A Cherepanov

Nils Grimsmo

Matthias Heiler

Fredrik Bergenlid

Karoly Csalogany

Aurelien Boffy

Yang Lu

Nicolo D'Ercole

Alvin Abdagic

Jennifer Daniel

Aurélien Boffy

Mehek Sharma

Adrien Olczak

Keqian Yan

Jackie Xiang

Jiawei Chen

Ajay Gokhale

Alexander Robertson

Dror Ayalon

Cristian Udrescu

Implementations described herein relate to causing emoji(s) that are associated with a given emotion class expressed by a spoken utterance to be visually rendered for presentation to a user at a display of a client device of the user. Processor(s) of the client device may receive audio data that captures the spoken utterance, process the audio data to generate textual data that is predicted to correspond to the spoken utterance, and cause a transcription of the textual data to be visually rendered for presentation to the user via the display. Further, the processor(s) may determine, based on processing the textual data, whether the spoken utterance expresses a given emotion class. In response to determining that the spoken utterance expresses the given emotion class, the processor(s) may cause emoji(s) that are stored in association with the given emotion class to be visually rendered for presentation to the user via the display.

System(s) and method(s) for causing contextually relevant emoji(s) to be visually rendered for presentation to user(s) in smart dictation

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining query refinements using search data. In one aspect, a method includes receiving a first query and a second query each comprising one or more n-grams for a user session, determining a first set of query refinements for the first query, determining a second set of query refinements from the first set of query refinements, each query refinement in the second set of query refinements including at least one n-gram that is similar to an n-gram from the first query and at least on n-gram that is similar to an n-gram from the second query, scoring each query refinement in the second set of query refinements, selecting a third query from a group consisting of the second set of query refinements and the second query, and providing the third query as input to a search operation.

Query refinements using search data

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating query suggestions based on search data. In one aspect, a method includes receiving, by one or more computers, a first query, determining query refinements based on the first query, generating, from the query refinements, refinement clusters, each refinement cluster corresponding to a particular topic and each refinement cluster including query refinements that are determined to belong to the particular topic to which the refinement cluster corresponds, ranking the refinement clusters, and selecting the refinement cluster that is highest in the ranking relative to other refinement clusters in the ranking as a first search refinement cluster for the first query.

Query suggestions based on search data

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Zurich, Switzerland

Alessandro Agostini