Google Inc.

Waymo LLC

Notion Labs, Inc.

Shir Judith Yehoshua

David Kliger Elson

David P Whipp

Ioan-Alexandru Sucan

Dong Li

Aida Khosroshahi

Matthew Paul McNaughton

Adrian Diaconu

Jack Whyte

Jing Qian

Ning Ma

Matthew Paul Mcnaughton

Alp Artar

Ryan Hart

Aspects of the technology relate to exception handling for a vehicle. For instance, a current trajectory for the vehicle and sensor data corresponding to one or more objects may be received. Based on the received sensor data, projected trajectories of the one or more objects may be determined. Potential collisions with the one or more objects may be determined based on the projected trajectories and the current trajectory. One of the potential collisions that is earliest in time may be identified. Based on the one of the potential collisions, a safety-time-horizon (STH) may be identified. When a runtime exception occurs, before performing a precautionary maneuver to avoid a collision, waiting no longer than the STH for the runtime exception to resolve.

Exception handling for autonomous vehicles

Systems and methods for deep connectivity between disparate database systems are described. User input to configure a content connectivity module includes a uniform resource identifier (URI) to a resource at a third party system. A bot may be configured with permissions to retrieve data at the resource, scan for links associated with third party objects, each link having a keyword associated with the content block management system. Identifiers of objects in the content block management system may be extracted from the links. Based on the identifiers, a relational connection between each block object and each third party object may be generated.

Deep connectivity between disparate database systems

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing dialogs. In one aspect, a method includes receiving a request associated with a task from a user device; submitting the request to each of a plurality of distinct data providers; receiving a plurality of suggested dialog responses from two or more of the data providers; scoring the one or more suggested dialog responses based on one or more scoring factors; determining a particular dialog response to provide to the user based on the scoring; and providing the determined dialog response to the user device.

Managing dialog data providers

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using frames for performing tasks. One of the methods includes receiving a first request to perform a task, the first request comprising user speech identifying the task; generating a frame associated with the task, wherein the frame comprises one or more types of values necessary to perform the task, and wherein each type of value can be satisfied by a respective value; receiving a second request to provide information related to a question, the second request comprising user speech identifying the question; providing information identifying the question to a search engine, and receiving a response identifying one or more terms; determining that at least one term can satisfy a type of value necessary to perform the task; and storing the at least one term in the frame.

Using frames for action dialogs

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing dialogs. In one aspect, a method includes receiving a request to perform a task from a user device; obtaining a dialog corresponding to the task; providing multiple protocol buffers to the user device; receiving a voice input and one or more annotated protocol buffers from the user device, the one or more annotated protocol buffers identifying corresponding non-verbal responses to content in the protocol buffers; and using the received protocol buffers to update a state of the dialog and to interpret the voice input.

Multimodal state circulation

Techniques are described herein for leveraging an online semantic processor to generate a finite state machine to be delivered to and implemented on a resource-constrained device. In various implementations, a user request may be received by a personal assistant module. Data indicative of the user request may be uploaded, e.g., by the personal assistant module, to an online semantic processor. Data may be received, e.g., by the personal assistant module, from the online semantic processor. The received data may represent a state machine having a plurality of candidate states of the personal assistant module. Each candidate state may be potentially responsive to the request. Resources local to the resource-constrained device may be analyzed to ascertain signal(s) unavailable to the online semantic processor. The personal assistant module may then transition to a given state of the plurality of candidate states. The given state may be selected based on the signal(s).

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San Francisco, CA, United States of America

Shir Judith Yehoshua