Google Inc.

Brian Chatham

Nitin Khandelwal

Akshay Goel

David Sanchez

Jonathan Eccles

Dmytro Lapchuk

Tal Dayan

Richard Park

Implementations described herein are directed to enabling collaborative ranking of interpretations of spoken utterances based on data that is available to an automated assistant and third-party agent(s), respectively. The automated assistant can determine first-party interpretation(s) of a spoken utterance provided by a user, and can cause the third-party agent(s) to determine third-party interpretation(s) of the spoken utterance provided by the user. In some implementations, the automated assistant can select a given interpretation, from the first-party interpretation(s) and the third-party interpretation(s), of the spoken utterance, and can cause a given third-party agent to satisfy the spoken utterance based on the given interpretation. In additional or alternative implementations, an independent third-party agent can obtain the first-party interpretation(s) and the third-party interpretation(s), select the given interpretation, and then transmit the given interpretation to the automated assistant and/or the given third-party agent.

Collaborative ranking of interpretations of spoken utterances

Distributed metrics collection systems and methods can be used to help bind together a large organization by implementing both formal and informal information flows. Hierarchical reporting and tasking are formal information flows. Gossip is an informal information flow. Users use a set of modules to configure distributed metrics collection. Management type users can configure data flows that appear similar to hierarchical reporting. All users can configure data flows that simply distribute items of interest. Over time, the data flows evolve in an organic manner as users appear, move, change interests, and leave.

Distributed metrics collection

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Fremont, CA, United States of America

Brian Chatham