Google Inc.

Todd Hester

Mark Rajan Malhotra

Jeffery Theodore Lee

Jeffrey Alan Boyd

Sophie Le Guen

Evan Jarman Fisher

George Alban Heitz

Allen Joseph Minich

Yoky Matsuoka

Jesse William Boettcher

Christopher Anthony Boross

Prashant Papiahgari Reddy

Sourav Raj Dey

Samuel W Kortz

Igor Karp

Mark Andrew McBride

Sayed Yusef Shafi

James P Lyons

Lev Stesin

Leon Tan

Oliver Fritz Lange

Piyush Khandelwal

David Horton

Andrew Urman

Jonathan Z Ben-Meshulam

Carles Pi-Sunyer

Alexander Dubman

Marc Nunkesser

Seongjae Lee

Gregory J Hu

Amanda Surya

Aubrey Thelen

Michael Soucie

Tim Enwall

Systems and methods are provided for predicting a traversal time for a route. A request for navigation directions is received and a route between the origin and destination is calculated. A series of route segments to be traversed on the route are identified, as well as one or more sub-series of route segments of the series of route segments, with each sub-series having route segments to be traversed consecutively on a portion of the route. For each sub-series, a sub-series traversal time is predicted based on historical traversal time data associated with historical trips where each of the route segments of the sub-series were traversed consecutively. A total route traversal time is predicted based on the sub-series traversal time. A set of navigation directions and the predicted total route traversal time are provided for presentation on a client device for navigating from the origin to the destination via the route.

Traversal time prediction for common user routes

Systems and Methods disclosed herein relate to providing a message to an application programming interface (API). The message includes a request for data from a data model, a submission of data to the data model, or both; and a host selection between: a representational state transfer (REST) host and a subscription-based application programming interface (API) host, wherein the REST host receives REST-based messages and the subscription-based API host receives messages in accordance with a standard of the subscription-based API host; wherein the request for data, the submission of data, or both are configured to create, delete, modify, or any combination thereof data related to a smart-device environment structure, a thermostat, a hazard detector, or any combination thereof stored in a data model accessible by the API.

Methods and apparatus for using smart environment devices via application program interfaces

Systems and techniques are provided for learned overrides for home security. A sensor of a security system may be armed. A trip signal may be received indicating a tripping of the sensor. It may be determined that the trip signal can be automatically overridden based on matching an identity of the sensor and a state of the security system with a pattern in a model. The pattern may represent a state of the security system in which automatically overriding the trip signal from the sensor is permitted. The trip signal from the sensor may be automatically overridden without input from a user.

Operating a security system

In an embodiment, an electronic device may include storage containing processor-executable instructions and a current setpoint schedule, and a processor configured to execute the instructions. The instructions may cause the processor to control an environmental control system based at least in part on the current setpoint schedule, and to determine the current setpoint schedule by detecting user behavior that indicates satisfaction with setpoints, based at least in part on the detected user behavior, determining a preference function that maps weights indicating user preferences for setpoints, determining candidate setpoint schedules, scoring the candidate setpoint schedules against the weights of the preference function to obtain first scores, where candidate setpoint schedules that best fit the weights of the preference function have the highest first scores, modifying the first scores to obtain second scores, and selecting the candidate setpoint schedule with the highest second score as a new current setpoint schedule.

Enhanced automated environmental control system scheduling using a preference function

Learned overrides for home security

In an embodiment, an electronic device may include a processor that may iteratively simulate candidate control trajectories using upper confidence bound for trees (UCT) to control an environmental control system (e.g., an HVAC system). Each candidate control trajectory may be simulated by selecting a control action at each of a plurality of time steps over a period of time that has the highest upper bound on possible performance using values from previous simulations and predicting a temperature for a next time step of the plurality of time steps that results from applying the selected control action using a thermal model. The processor may determine a value of each candidate control trajectory using a cost function, update the value of each control action selected in each candidate control trajectory, and select a candidate control trajectory with the highest value using UCT to apply to control the environmental control system.

Predictively controlling an environmental control system

In an embodiment, an electronic device may include storage containing processor-executable instructions, a preference function that maps weights indicating likely user preferences for the range of values of a device setting in relation to a range of values of a variable, and a current automated device control schedule configured to control the device setting of the electronic device in relation to the variable, and a processor. The instructions may cause the processor to determine the current automated device control schedule based on the preference function by detecting user behavior that indicates satisfaction or dissatisfaction with values of the device setting in relation to the variable, updating the preference function based on the detected user behavior, and determining the current automated device control schedule by comparing a number of candidate device control schedules against the weights of the preference function and selecting the candidate with the highest score.

Enhanced automated control scheduling

A peer group of a user may be identified on the basis of geographic information and building occupancy information for the user and for possible peers of the user. Energy usage data for each peer group member may be compared with that of the user. A comparison result may be computed between each peer group member and the user. A representation of comparison results may be provided to the user.

Identification of similar users

Growing community of inventors

San Francisco, CA, United States of America

Todd Hester