Abstract: Described herein is a system for predictive feature analysis to precompute and store data required to respond to a user input in advance of receiving the user input. To determine when to precompute the data, the system uses a prediction model to predict user interactions and when to expect the user input. The system predicts that a user input is about to be received, and starts to process certain data to determine feature data and stores the data in a cache. When the user input is received, the system retrieves the data from the cache for further processing to respond to the user input.

Abstract: Systems and methods for state prediction of devices are disclosed. A group of reference devices may be identified and a subset of the reference devices may be identified and/or determined based at least in part on a degree of similarity between reference usage-patterns associated with the reference devices and a usage pattern of a target device. The current state of the subset of the reference devices may be determined and may be utilized to determine a probability that the target device should be in a given state. The state prediction information may be utilized for one or more actions, such as sending recommendations, target inference operations, and/or device configuration, for example.

Abstract: Systems and methods for activity-based device recommendations are disclosed. For example, historical usage data associated with a device may indicate that the device is likely to be associated with a given state at a given time. When the device is not in the anticipated state, a recommendation to transition the device state, for example, may be sent. Additionally, a determination of the activity state associated with the device, such as an active state, an asleep state, and/or an away state may be utilized to determine the recommendation to surface, to determine whether to send a recommendation, and when and/or how to send the recommendation.

Abstract: Systems and methods for usage-based device naming and grouping are disclosed. For example, trigger events that indicate when a device should be renamed, added to a device group, and/or added to a routine may be determined. Usage data representing usage of the device may be received and utilized to determine if a trigger event occurs. When a trigger event occurs, a recommendation for renaming, grouping, etc. may be determined and sent to a user device. Upon acceptance of the recommendation, the device may be renamed, grouped, and/or added to a routine.

Abstract: A method and device for automatic meeting detection and analysis. A mobile electronic device includes multiple sensors configured to selectively capture sensor data. A classifier is configured to analyze the sensor data to detect a meeting zone for a meeting with multiple participants. A processor device is configured to control the multiple sensors and the classifier to trigger sensor data capture.

Abstract: This disclosure describes, in part, techniques for utilizing global models to generate local models for electronic devices in an environment, and techniques for utilizing the global models and/or the local models to provide notifications that are based on anomalies detected within the environment. For instance, a remote system may receive an identifier associated with an electronic device and identify a global model using the identifier. The remote system may then receive data indicating state changes of the electronic device and use the data and the global model to generate a local model associated with the electronic device. Using the global model and/or local model, the remote system can identify anomalies associated with the electronic device and, in response to identifying an anomaly, notify the user. The remote system can further cause the electronic device to change states after receiving a request from the user.

Abstract: Systems and methods allow for floor plan generation for device visualization and use. For example, image data may be utilized to segment a building from non-building elements in an image, and the outline of the building may be utilized to generate a floor plan of the building. A user interface may be generated to present the floor plan and allow for placement of representations of walls, doors, windows, and electronic devices within the floor plan. Placement of these elements may be performed in response to input from a user and/or may be performed automatically utilizing naming indicators, device-affinity data, historical usage data, signal-strength data, etc. Once placed, the device representations may be utilized to operate the devices and display related information, such as alerts.

Abstract: Systems and methods for intelligent device grouping are disclosed. An environment, such as a home, may have a number of voice-enabled devices and accessory devices that may be controlled by the voice-enabled devices. One or more models, such as linguistics model(s) and/or device affinity models may be utilized to determine which accessory devices are candidates for inclusion in a device group, and a recommendation for grouping the devices may be provided. Device-group naming recommendations may also be generated and may be sent to users.

Abstract: Systems and methods for usage-based device naming and grouping are disclosed. For example, trigger events that indicate when a device should be renamed, added to a device group, and/or added to a routine may be determined. Usage data representing usage of the device may be received and utilized to determine if a trigger event occurs. When a trigger event occurs, a recommendation for renaming, grouping, etc. may be determined and sent to a user device. Upon acceptance of the recommendation, the device may be renamed, grouped, and/or added to a routine.

Abstract: Systems and methods for device naming-indicator generation are disclosed. Friendly names for accessory devices, such as smart-home devices, may be utilized to generate formatted text data that includes capitalization and/or punctuation for the friendly names. The formatted text data may be utilized to generate tag data indicating attributes of the friendly name. The tag data and/or contextual data indicating historical usage of the accessory device may be utilized to generate naming indicator(s) for the accessory device. The naming indicator(s) may be utilized, for example, during target inference and/or for communicating with a user about the accessory device.

Abstract: Devices, systems and methods are disclosed for improving facial recognition and/or speaker recognition models by using results obtained from one model to assist in generating results from the other model. For example, a device may perform facial recognition for image data to identify users and may use the results of the facial recognition to assist in speaker recognition for corresponding audio data. Alternatively or additionally, the device may perform speaker recognition for audio data to identify users and may use the results of the speaker recognition to assist in facial recognition for corresponding image data. As a result, the device may identify users in video data that are not included in the facial recognition model and may identify users in audio data that are not included in the speaker recognition model. The facial recognition and/or speaker recognition models may be updated during run-time and/or offline using post-processed data.

Abstract: Described are systems, methods, and apparatus that gathers environment condition data from different sensors at various locations within an area, aggregates the environment condition data to produce aggregated environment condition scores for the area and provides the aggregated environment condition scores to different locations within the area. While sensor data from a single sensor/device, such as a camera may provide low quality environment information, by collecting and aggregating information from multiple sensors and/or locations in the area, highly accurate aggregated environment condition scores for environment conditions may be realized. The aggregated environment condition scores may be provided to various locations within the area as representative of the environment condition at that point in time within the area, regardless of whether those locations have sensors.

Abstract: Systems and methods for target-device resolution are disclosed. A user may speak a user utterance requesting an action to be performed with respect to an accessory device, such as a smart-home device. The user utterance may include an identifier for the accessory device, but that identifier may not correspond to a naming indicator of an accessory device and/or may correspond to multiple naming indicators. In these examples, one or more models are utilized to identify which accessory device is most likely to correspond to the accessory device targeted by the user utterance for operation.

Abstract: Systems and methods for usage-based device naming and grouping are disclosed. For example, trigger events that indicate when a device should be renamed, added to a device group, and/or added to a routine may be determined. Usage data representing usage of the device may be received and utilized to determine if a trigger event occurs. When a trigger event occurs, a recommendation for renaming, grouping, etc. may be determined and sent to a user device. Upon acceptance of the recommendation, the device may be renamed, grouped, and/or added to a routine.

Abstract: Systems and methods for state prediction of devices are disclosed. A group of reference devices may be identified and a subset of the reference devices may be identified and/or determined based at least in part on a degree of similarity between reference usage-patterns associated with the reference devices and a usage pattern of a target device. The current state of the subset of the reference devices may be determined and may be utilized to determine a probability that the target device should be in a given state. The state prediction information may be utilized for one or more actions, such as sending recommendations, target inference operations, and/or device configuration, for example.

Abstract: This disclosure describes, in part, techniques for utilizing global models to generate local models for electronic devices in an environment, and techniques for utilizing the global models and/or the local models to provide notifications that are based on anomalies detected within the environment. For instance, a remote system may receive an identifier associated with an electronic device and identify a global model using the identifier. The remote system may then receive data indicating state changes of the electronic device and use the data and the global model to generate a local model associated with the electronic device. Using the global model and/or local model, the remote system can identify anomalies associated with the electronic device and, in response to identifying an anomaly, notify the user. The remote system can further cause the electronic device to change states after receiving a request from the user.

Abstract: A video capture device may include multiple cameras that simultaneously capture video data. The video capture device and/or one or more remote computing resources may stitch the video data captured by the multiple cameras to generate stitched video data that corresponds to 360° video. The remote computing resources may apply one or more algorithms to the stitched video data to identify one or more frames that depict content that is likely to be of interest to a user. The video capture device and/or the remote computing resources may generate one or more images from the one or more frames, and may send the one or more images to the user.

Abstract: Devices, systems and methods are disclosed for improving facial recognition and/or speaker recognition models by using results obtained from one model to assist in generating results from the other model. For example, a device may perform facial recognition for image data to identify users and may use the results of the facial recognition to assist in speaker recognition for corresponding audio data. Alternatively or additionally, the device may perform speaker recognition for audio data to identify users and may use the results of the speaker recognition to assist in facial recognition for corresponding image data. As a result, the device may identify users in video data that are not included in the facial recognition model and may identify users in audio data that are not included in the speaker recognition model. The facial recognition and/or speaker recognition models may be updated during run-time and/or offline using post-processed data.

Abstract: Social grouping using a device may include determining, by the device, a set of attributes associated with interactions between a user and a set of contacts, wherein the set of attributes associated with the interactions is related to the device. The contacts may be organized into groups. The groups may be hierarchically ordered with at least one of the groups being a subgroup of another one of the groups.

Abstract: A viewing device, such as a virtual reality headset, allows a user to view a panoramic scene captured by one or more video capture devices that may include multiple cameras that simultaneously capture 360° video data. The viewing device may display the panoramic scene in real time and change the display in response to moving the viewing device and/or changing perspectives by switching to video data being captured by a different video capture device within the environment. Moreover, multiple video capture devices located within an environment can be used to create a three-dimensional representation of the environment that allows a user to explore the three-dimensional space while viewing the environment in real time.