The Adt Security Corporation

Adt US Holdings, Inc.

Shy Ward

Mark Reimer

Thomas Nakatani

Dmitry Vaynriber

Bergen Davell

Robert Beaver

Clinton Masterson

Andrew Droney

Arthur Orduna

Cynthia Haegley

Neil Brown

According to some embodiments, a control device for a premises security system that is configured to monitor a premises using a plurality of premises devices is provided. The control device is configured to obtain a plurality of acoustic samples for the premises, detect a sound anomaly in at least one of the plurality of acoustic samples, obtain a verification that the sound anomaly is expected, generate an acoustic model for the premises based at least on the plurality of acoustic samples and the verification that the sound anomaly is expected, receive data representing a detected sound during monitoring of the premises, compare the detected sound with the acoustic model for the premises to determine that the detected sound is unexpected, and initiate a premises security system alert based at least on the detected sound being unexpected.

Premises monitoring using acoustic models of premises

According to one or more embodiments, an interaction device is provided. The interaction device includes processing circuitry configured to render for display a first premises security video comprising a plurality of frames, determine a user interaction with a playback of the first premises security video, determine a plurality of logical weights associated with the plurality of frames based at least on the user interaction, train a machine learning model based at least on the plurality of logical weights, and perform a premises security system action based at least on the trained machine learning model.

Determining areas of interest in video based at least on a user's interactions with the video

Method, devices and systems are described. A diagnostics system for a premises monitoring system at a premises is provided. The diagnostic system includes processing circuitry configured to: receive premises system data associated with the premises monitoring system, perform supervised learning of a deep learning process based at least in part on the premises system data, and determine a classified set of resolutions based at least in part on the performed supervised learning where each resolution of the classified set of resolutions is associated with at least one premises event, and the at least one premises event relates to the premises monitoring system.

Customer experience diagnostics in premises monitoring systems

A server system is described. The server system includes at least one processor and at least one memory storing a plurality of instructions that, when executed by the at least one processor, causes the at least one processor to receive a plurality of machine learning (ML) models and ML model data from the plurality of mobile computing devices, where the ML model data includes metadata, and the ML model data excludes media. At least one group of the plurality of ML models is selected based on the metadata, each ML model of the at least one group tested, and a plurality of metrics is determined based on the ML models that were tested. Further, a set of ML models of the ML models that were tested is selected, and at least two ML models of the set of ML models are combined based on at least one parameter.

Machine learning model inference using user-created machine learning models while maintaining user privacy

A system for interacting with a portable device is provided. The system is configured to process both personal and premises events, and includes at least one processor configured to generate device data for determining the location of the portable device and process sensor data in response to an event. The processor is further configured to generate situational data for the processed event. The situational data is based on the sensor data and the device data. The situational data includes information indicative of whether the event is an alarm event and if the event is the alarm event, whether the alarm event is one of a personal event and premises event. The processor is further configured to provide at least a portion of the situational data to the remote service site if the event is the alarm event and determine that confirmation has been received.

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Flower Mound, TX, United States of America

Shy Ward