Abstract: At least one device can be classified based on user activity. At least one telemetry variable with respect to time may be received for the at least one device. The at least one telemetry variable with respect to time may be used to determine an activity model for the at least one device. Based on the activity model, a probability of the at least one device belonging to a profile or to a type of user may be determined. Based on the type of user of the at least one device, a setting associated with the at least one device may be changed.

Abstract: Systems, apparatuses, and methods are described for detection of an item being delivered at a premises. Based on a movement pattern shown in a video captured by a camera associated with a premises, a delivery event may be determined. A notification of the delivery event may be sent to a user device associated with the premises.

Abstract: A device may be configured to determine whether an audio file is a first type of audio file that is capable of being processed to recognize the voice query based on a characteristic of the audio file itself or a second type of audio file that may require speech recognition processing in order to recognize the voice query associated with the audio file. In determining whether the audio file is a first type of audio file or a second type of audio file, a query filter associated with the device may be configured to access one or more guidance queries. Using the one or more guidance queries, the device may classify the audio file as a first type of audio file or a second type of audio file based on receiving only a portion of the audio file, thereby improving the speed at which the audio file can be processed.

Abstract: User enrollment to a biometric identification system begins on selected general input devices (GID) such as smartphones. The user may enter identification data (e.g. name) and use a red-green-blue (RGB) camera of the GID to acquire a first image (e.g. hand). The first image is processed using both a first model to determine a first representation and a second model to determine a second representation. Upon presentation of a hand at a biometric input device, a second image is acquired using a first modality and a third image is acquired using a second modality. The second image is processed using the first model to determine a third representation. The third image is processed using the second model to determine a fourth representation. Given a match between both the first and third representations, as well as the second and fourth representations, enrollment is completed by storing the third and fourth representations.

Abstract: At least one device can be classified based on user activity. At least one telemetry variable with respect to time may be received for the at least one device. The at least one telemetry variable with respect to time may be used to determine an activity model for the at least one device. Based on the activity model, a probability of the at least one device belonging to a profile or to a type of user may be determined. Based on the type of user of the at least one device, a setting associated with the at least one device may be changed.

Abstract: A camera system may automatically identify high frequency motion regions in a field of view of the camera system. Analysis of motion events detected at different regions of a scene in the field of view, may be performed. Similar events frequently occurring at a certain region may be recorded/accumulated and compared to a threshold. Regions with high frequency activity may be determined and a user may be notified. If the user is not interested in events within the high activity regions, the regions may be ignored and notifications to the user may cease. The camera system may determine specific actions/events occurring in a region, such as a person approaching or leaving a home.

Abstract: Systems, apparatuses, and methods are described for presence detection of a wireless device in a sensing region. Communication parameters determined by a computing device over one or more periods may be used to generate criteria for determining the occurrence of enter events or leave events. The criteria may comprise, for values of one or more communication parameters, rates of change associated with movement into or out of the sensing region. The criteria may be compared against subsequent data associated with the communication parameters to determine if an enter event or a leave event has occurred.

Abstract: Methods, systems, and apparatuses for predicting an end of a command in a voice recognition input are described herein. The system may receive data comprising a voice input. The system may receive a signal comprising a voice input. The system may detect, in the voice input, data that is associated with a first portion of a command. The system may predict, based on the first portion and while the voice input is being received, a second portion of the command. The prediction may be generated by a machine learning algorithm that is trained based at least in part on historical data comprising user input data. The system may cause execution of the command, based on the first portion and the predicted second portion, prior to an end of the voice input.

Abstract: Systems, apparatuses, and methods are described for presence detection of a wireless device in a sensing region. Communication parameters determined by a computing device over one or more periods may be used to generate criteria for determining the occurrence of enter events or leave events. The criteria may comprise, for values of one or more communication parameters, rates of change associated with movement into or out of the sensing region. The criteria may be compared against subsequent data associated with the communication parameters to determine if an enter event or a leave event has occurred.

Abstract: A camera system may automatically identify high frequency motion regions in a field of view of the camera system. Analysis of motion events detected at different regions of a scene in the field of view, may be performed. Similar events frequently occurring at a certain region may be recorded/accumulated and compared to a threshold. Regions with high frequency activity may be determined and a user may be notified. If the user is not interested in events within the high activity regions, the regions may be ignored and notifications to the user may cease. The camera system may determine specific actions/events occurring in a region, such as a person approaching or leaving a home.

Abstract: An audio file associated with a user voice query may be received at a user device. The audio file may be compared to a plurality of references, such as cache entries, corresponding to a plurality of other voice queries. Based on a determination that the voice query corresponds to one of the references, an operation associated with the voice query may be executed. An indication may be received that the operation was not an intended operation associated with the voice query. Based on receiving this indication, the incorrectly identified operation, associated reference, e.g., voice query, may be disabled for the user or the device. However, the cache entry may remain enabled for one or more of a plurality of other devices.

Abstract: Methods and systems are described for recognizing, based on a voice input, a user and/or a voice command. An algorithm is described herein that processes data associated with a voice input. The data may indicate characteristics of the voice such as a gender, an age, or accent associated with the voice and other metadata. For example, the system may process the data and determine the gender of a voice. The determined characteristics may be used as an input into a voice recognition engine to improve the accuracy of identifying the user who spoke the voice input and identifying a voice command associated with the voice input. For example, the determined gender may be used as a parameter to improve the accuracy of an identified user (e.g., the speaker) or command. The algorithm may adjust, based on gender, parameters such as confidence thresholds used to match voices and voice commands.

Abstract: Methods and systems are disclosed for estimating locations of devices operating on a network (such as a wireless network) in a premises. Signal strength measurements of signals sent by the devices may be used to estimate locations of the devices within the premises as well as to estimate anchor points where signals are received from the devices. The devices may be shown on a map of the premises. The anchor points may be shown on a map of the premises. A method may be implemented iteratively to estimate the device locations as well as the locations of the anchor points. Estimated locations of devices and/or anchor points may be used to estimate a location of a device misplaced or lost in the premises.

Abstract: Systems, apparatuses, and methods are described for detecting motion and/or objects in images using distance data. An image of an area to be monitored may be used to generate a distance map that maps distances between features in the area and a camera that took the image. Different reactions may be performed based on motion and/or objects detected in images from the camera and determined to be within different distance reaction zones relative to the camera. The different distance reaction zones and reactions may be based on customizable reaction rules. The distance reaction zones and/or reaction rules may be suggested based on similarities between the area to be monitored and other monitored areas.

Abstract: A cache associated with a voice recognition engine may be configured to receive an audio file corresponding to a voice query. The voice query may be received by the voice recognition engine from a user device in response to receipt of the voice query by the user device at the user device. The cache may be configured to determine an audio fingerprint based on the audio file and to determine whether the audio fingerprint corresponds to one of a plurality of audio transcriptions stored in the cache. The audio transcriptions may correspond to popular voice queries received at the voice recognition engine and processed by a server capable of performing automated speech recognition. In response to determining that the audio fingerprint corresponds to a given one of the stored audio transcriptions, the audio file may be processed based on the stored audio transcription.

Abstract: Monitoring a premises may comprise receiving signals from a plurality of devices. One or more spatially static devices of the plurality of devices may be determined based on strengths of the received signals. A user activity pattern may be determined based on changes in strengths of signals received from the spatially static devices. Abnormal user activity may be determined based on the user activity pattern and a strength of at least one signal received from at least one of the spatially static devices.

Abstract: Methods, apparatuses, and systems are described for capturing images of a scene or environment. Parameters may be determined for a first area and a second area of a captured image. Based on the parameters, an image capture mode may be enabled. Images may be captured based on the image capture mode.

Abstract: Methods, systems, and apparatuses for predicting an end of a command in a voice recognition input are described herein. The system may receive data comprising a voice input. The system may receive a signal comprising a voice input. The system may detect, in the voice input, data that is associated with a first portion of a command. The system may predict, based on the first portion and while the voice input is being received, a second portion of the command. The prediction may be generated by a machine learning algorithm that is trained based at least in part on historical data comprising user input data. The system may cause execution of the command, based on the first portion and the predicted second portion, prior to an end of the voice input.

Abstract: Methods and systems for more efficient analyses of and response to voice commands and queries are provided. The system may be configured to receive one or more of audio files corresponding to a voice query and determine, for each of the audio files, whether the audio file is a first type of audio file capable of being processed based on a characteristic of the audio file or a second type of audio file that cannot, and may require further processing in order to recognize the voice query associated with the audio file. The system may process each of the first type of audio files and respond to the associated voice queries. The system may also determine a priority for each of the second type of audio files for further processing of the second type of audio files.

Abstract: Methods and systems are disclosed for estimating locations of devices operating on a network (such as a wireless network) in a premises. Signal strength measurements of signals sent by the devices may be used to estimate locations of the devices within the premises as well as to estimate anchor points where signals are received from the devices. The devices may be shown on a map of the premises. The anchor points may be shown on a map of the premises. A method may be implemented iteratively to estimate the device locations as well as the locations of the anchor points. Estimated locations of devices and/or anchor points may be used to estimate a location of a device misplaced or lost in the premises.