Google Inc.

Nest Labs, Inc.

Mark Rajan Malhotra

Yash Modi

Yoky Matsuoka

Jeffery Theodore Lee

Evan Jarman Fisher

Sophie Le Guen

Sourav Raj Dey

Mark D Stefanski

Kevin Charles Peterson

Timo A Bruck

Jeffrey Alan Boyd

Joseph Adam Ruff

Prashant Papiahgari Reddy

Anthony Michael Fadell

Maxime Veron

Allen Joseph Minich

Jesse William Boettcher

Kenneth Louis Herman

Rangoli Sharan

Frank Emmanuel Astier

Lawrence Au

Dongeek Shin

Michael D Dixon

Todd Hester

Gwendolyn Van Der Linden

David Hendler Sloo

Bryan J James

Aveek Ravishekhar Purohit

Kristoffer John Donhowe

Laura Rabb

Michael Plitkins

Ehsan Maani

James B Simister

Patrick M Lister

James Eric Mason

Julia R Deluliis

Clemens M Knieper

Shao-Po Ma

Ken Herman

Josh Buffum

David Louis Warner

Matthew Lee Rogers

Matthew S Rogers

Shigefumi Honjo

Isabel I Guenette

Lauren Von Dehsen

Christopher Anthony Boross

Nina F Shih

Michael J Lammers

Samuel W Kortz

Igor Karp

Mark Andrew McBride

James P Lyons

Lev Stesin

Leon Tan

Carles Pi-Sunyer

David Horton

Greg Fulco

Jason Lee

Andrew Urman

Kara Malhotra

Alexander Dubman

Julia Deiuliis

Allen J Minich

Gregory J Hu

Amanda Surya

Aubrey Thelen

Michael Soucie

Tim Enwall

Andrew Hengel

Sourav Dey

A control system includes an energy management system in operation with intelligent, network-connected thermostats located in structures. The thermostats are operable to control heating, ventilation, and air conditioning (HVAC) systems. Control during a demand response (DR) event period may be performed based on an optimal setpoint schedule of the HVAC system. Candidate setpoint schedules may be simulated to identify an optimal schedule.

Controlling an HVAC system during a demand-response event with display of effective setpoints

A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.

Method, apparatus and system for passive infrared sensor framework

A control system includes an energy management system in operation with intelligent, network-connected thermostats located in structures. The thermostats are operable to control heating, ventilation, and air conditioning (HVAC) systems. Control during a demand response (DR) event period may be performed based on an optimal control trajectory of the HVAC system, where the control trajectory is optimal in that it minimizes a cost function.

Controlling an HVAC system using an optimal setpoint schedule during a demand-response event

A method includes a system that operates according to a control schedule; detects events that indicate occupancy; stores a record of the events that indicate occupancy in one or more memory devices; and enters an auto-away state. A determination to enter the auto-away state may be based at least in part on a length of a time interval during which no events that indicate occupancy were detected and the stored record of the events that indicate occupancy. The system may also detect a pattern of instances where the auto-away state is entered over a plurality of days and adjusts the control schedule based at least in part on the pattern of instances where the auto-away state is entered.

Automated presence detection and presence-related control within an intelligent controller

An image of at least a portion of a room may be received, the image of the room comprising an image of a sensor mounted in the room. At least one optical parameter related to the image of the room may also be received. A distance may be determined between the sensor and a camera that captured the image of the room, wherein the determination of the distance is based at least in part on the optical parameters and on known physical dimensions of the sensor. A sensitivity requirement of the sensor may be determined, based on the distance. The determined sensitivity may be sent to control logic of the sensor.

Determining sensor installation characteristics from camera image

Systems and techniques are provided for sensor bypass. Activation may be received at a bypass input of an entry point sensor of a security system while the entry point sensor is in an armed mode. The entry point sensor may detect that the entry point monitored by the entry point sensor is closed. The entry point sensor may enter into a bypass mode. Detection by the entry point sensor of an opening of the entry point while the entry point sensor is in the bypass mode may not result in the generation of an alarm by the security system.

Sensor bypass

In a method for detection of an anomaly related to information about a location of a mobile computing device, the anomaly related to the information about the location of the mobile computing device can be detected by a processor of the mobile computing device. A communication about the anomaly can be provided via a user interface of the mobile computing device. The anomaly can be caused by a bug in an operating system of the mobile computing device. Notifications about the anomaly can be provided to a user of the mobile computing device or other mobile computing devices registered to a shared account.

Detection of anomaly related to information about location of mobile computing device

Systems and methods of detecting human movement with a sensor are provided, including generating a motion event signal in response to movement detected by the sensor, and generating a parameterized curve to represent the detected motion. The parameterized curve is fit to a predetermined window of sensor data captured by the sensor to filter the motion event signal. A noise magnitude estimate and a curve fit error is determined based on the fitted parameterized curve to the predetermined window. A detection threshold value is determined based on the curve fit error, a noise source signal estimate of known noise, and zero or more noise magnitudes from other sources. Human motion is determined by correlating a true motion event signal with human motion based on a comparison between a value of a point on the parameterized curve and the detection threshold value.

Systems and methods of motion detection using dynamic thresholds and data filtering

The current application is directed to intelligent controllers that use sensor output and electronically stored information to determine whether or not one or more types of entities are present within an area, volume, or environment monitored by the intelligent controllers. The intelligent controllers select operational modes and/or modify control schedules with respect to the presence and absence of the one or more entities. The intelligent controllers selectively carry out scheduled control operations during periods of time when one or more types of entities are determined not to be in a controlled environment.

Selective carrying out of scheduled control operations by an intelligent controller

Controlling an HVAC system in association with a demand-response event

Apparatus, systems, methods, for optimizing a schedule of setpoint temperatures used in the control of an HVAC system are presented herein. A schedule of setpoint temperatures may be adjusted by small, incremental amounts so that the schedule migrates from the original schedule to a more energy-efficient schedule.

Automated adjustment of an HVAC schedule for resource conservation

A method includes operating a controller according to a control schedule; detecting events that indicate occupancy; storing a record of the events that indicate occupancy in one or more memory devices; and causing the controller to enter an auto-away state. A determination that the controller should enter the auto-away state may be based at least in part on a length of a time interval during which no events that indicate occupancy were detected; and the stored record of the events that indicate occupancy. The method also includes detecting a pattern of instances where the controller enters the auto-away state over a plurality of days; and adjusting the control schedule based at least in part on the pattern of instances where the controller enters the auto-away state.

An electronic device associated with a lock device detects a trigger event related to security of a smart home environment, determines a target state of the lock device based on the trigger event, and in accordance with a determination that the target state indicates a security threat: determines a current state of the lock device, compares the target state of the lock device with the current state of the lock device, and provides or foregoes provision of instructions to operate the lock device based on a result of the comparison.

Systems and methods for anticipatory locking and unlocking of a smart-sensor door lock

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San Mateo, CA, United States of America

Mark Rajan Malhotra