Amazon Technologies, Inc.

Charles Edwin Ashton Brett

William Evan Welbourne

Sven Eberhardt

Akshay Kumar

Sara Parker Hillenmeyer

Aniruddha Basak

Hongyang Wang

Lizhen Peng

George Strajan

Zeya Chen

Jay Patel

Yunfeng Jiang

Clare Elizabeth Veladanda

Jatin Bajaj

Amir Salimi

Paul Aksenti Savastinuk

Maisie Wang

Marc Wetter

Sunny Singh

Chandra Prakash Konkimalla

Yishuai Li

Akanksha Gupta

Jin Long Lee

Meet Prakash Vadera

Shekhar Pareek

Siyuan Liu

Arpit Jain

Suhel Banerjee

Rajesh Bangaru Ravindranath

Systems and methods for floor plan generation for device visualization and use are disclosed. 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.

Floor plan generation for device visualization and use

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.

Intelligent device grouping

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.

Activity-based device recommendations

Systems and methods for device control using near real time learning are disclosed. For example, an automatic action is performed by a target device in response to a predefined condition being met. Thereafter, context data is gathered and utilized to determine whether a negative user reaction has been provided in response to performance of the automatic action. When a negative user reaction is determined, mitigating actions may be taken close in time to when the context data is received to prevent further negative user reactions.

Device control using near real time modeling

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.

Target-device resolution

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.

State prediction of devices

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.

Systems and methods for device functionality control utilizing activity prediction are disclosed. For example, an activity model is utilized to identify activities associated with sensor data collected from connected devices within an environment. Target device usage data is then associated with the activities to determine probability values that the target device will be operated when the identified activities are detected. Commands may be generated and sent to the target device to perform a function when the identified activities are detected.

Device functionality control utilizing activity prediction

Techniques for automatically combining devices into a single group of devices, and splitting devices into multiple groups of devices are described. A machine learning model may process device profile data, associated with devices registered to two different users, and determine the devices should be combined into a single group of devices. Such enables a user to control each of the devices, of the two different users, but providing user inputs to a single device of the group. The machine learning model may also process device profile data, associated with devices registered to a single user, and determine the devices should be split into two or more groups of devices. Such may decrease the likelihood that a system may inadvertently control a device not intended by a user.

Automated device grouping

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. The aggregated environment condition scores may be used by other devices at those locations to automate one or more actions, such as adjusting lighting conditions, closing garage doors, adjusting window blind positions, etc.

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Seattle, WA, United States of America

Charles Edwin Ashton Brett