Google Inc.

Ami Patel

Mohammed Waleed Kadous

Brian Patrick Williams

Andrew Ames Bunner

Nadav Aharony

Pascal Tom Getreuer

Marc Stogaitis

Alan Lee Gardner, Iii

Zhentao Sun

Richard Daniel Webb

Thomas Chao

Brian Duff

Daniel Estrada Alva

Benjamin Razon

Tony Weber

Liem Tran

Joseph A Farfel

Richard Francis Lyon

Ryan Michael Rifkin

Siamak Tazari

Denise Ho

Ralph Jacob Cressman

Computer generated speech can be generated for cross-lingual natural language textual data streams by utilizing a universal phoneme set. In a variety of implementations, the natural language textual data stream includes a primary language portion in a primary language and a secondary language portion that is not in the primary language. Phonemes corresponding to the secondary language portion can be determined from a set of phonemes in a universal data set. These phonemes can be mapped back to a set of phonemes for the primary language. Audio data can be generated for these phonemes to pronounce the secondary language portion of the natural language textual data stream utilizing phonemes associated with the primary language.

Speech generation using crosslingual phoneme mapping

The disclosure includes a system and method for detecting fine grain copresence between users. The system includes a processor and a memory storing instructions that when executed cause the system to: transmit a wakeup signal to a plurality of devices based on coarse grain location information; send a request to a first device of the plurality of devices to transmit a token using a first communication technology to determine fine grain copresence; receive a first token acknowledgment from a first subset of the plurality of devices; send a request to a second device of the first subset of the plurality of devices to transmit the token using a second communication technology to determine fine grain copresence; receive a second token acknowledgment from a second subset of the plurality of devices; and refine copresence based on receiving the first and second token acknowledgment.

Server orchestrated connectivity

The disclosure includes a system and method for detecting fine grain copresence between users. The system includes a processor and a memory storing instructions that when executed cause the system to receive user input regarding copresence detection settings for a first user device, the copresence detection settings comprising a location and/or a user access control list, and determine a current location of the first user device. The system may determine whether copresence detection of the first user device is enabled at the current location based on the copresence detection settings and the current location. Based on determining that copresence detection is enabled, the system may refine copresence and perform an action based on fine grain copresence of the first and second user device.

Copresence permission model

The disclosure includes a system and method for detecting fine grain copresence between users. The system includes a processor and a memory storing instructions that when executed cause the system to: process one or more signals to determine coarse grain location information of a first device and a second device; determine whether the first device and the second device are copresent based on the coarse grain location information; in response to determining that the first device and the second device are copresent based on the coarse grain location information, transmit a signal to the second device to alert the second device to listen for a fine grain copresence token from the first device; and refine copresence based on receiving an indication that the second device has received the fine grain copresence token.

Two-pass copresence

Growing community of inventors

Mountain View, CA, United States of America

Ami Patel