Amazon Technologies, Inc.

Samsara Inc.

William Evan Welbourne

Charles Edwin Ashton Brett

Akshay Kumar

Sara Parker Hillenmeyer

Sven Eberhardt

Aniruddha Basak

Paul Aksenti Savastinuk

George Strajan

Cheng-Hao Kuo

Ross David Roessler

Hongyang Wang

Yinfei Yang

Jim Oommen Thomas

Lizhen Peng

Jay Patel

Hersh Sridhar Iyer

Tsz Ho Yu

Zeya Chen

Yunfeng Jiang

Yu Liu

Jing Wang

Clare Elizabeth Veladanda

Jatin Bajaj

Vinay Kotikalapudi Sriram

Sunny Singh

Marc Wetter

Chandra Prakash Konkimalla

Yishuai Li

David Y Zhao

Matthew Alan Townsend

Ingo Gerhard Wiegand

Sharan Srinivasan

Greg Allen Cummings

William Nathan John Hurst

Margaret Irene Finch

Shirish Nair

Deon Poncini

Geoff Hunter Donaldson

Narendran Rajan

Amir Salimi

Meet Prakash Vadera

Jennifer Liwen Chen

Min Hao Chen

Siyuan Liu

Arpit Jain

Rajesh Bangaru Ravindranath

Shirish R Nair

Xiaodong Tian

Jagrut Arora

Siddharth Heroor

Faraz Abbasi

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

The present disclosure is related to systems and methods of dashcam installation for providing instructions on placing a dashcam for a person or dashcam installer. Aspects of the present disclosure are related to providing a digital assistant for installing the dashcam in an acceptable position, where the dashcam can capture images of objects with acceptable quality (e.g., as determined by a machine learning model). In some embodiments, the dashcam detects a driver and provides inferences related to the current dashcam position. The inferences can be based on analyzing images captured by the dashcam in the current position, where the analysis can utilize a machine learning model. The inferences can also provide one or more instructions to move and/or tilt the dashcam to the acceptable installation position.

Systems and methods for dashcam installation

Methods, systems, and apparatuses capture event video segments from an IoT device such as a dashcam in a vehicle. When an even is detected, the original resolution video segment is converted to a low resolution video segment and stored in a cloud backend. Some embodiments thus save bandwidth and storage space by transmitting low resolution video segment to the cloud backend and further save bandwidth and storage space by only storing video in the cloud backend when an event is detected. Bandwidth is also saved because, in some embodiments, original resolution video segments are stored on the IoT device and only transmitted to a reviewer when requested. In the cloud backend, the low resolution video segment is converted to a higher resolution video using a deep learning video upscaler before it is reviewed by a reviewer. A human reviewer can also review the original resolution video segment that is stored on the IoT device if it is desired to see the video segment in its original resolution. In some embodiments, a reviewer can request that an area of interest in the video segment being reviewed is upscaled. In such embodiments the entire low resolution video segment is not always upscaled in its entirety before review.

Optimizing video data upload for dashcam event reviews

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 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.

Device naming-indicator generation

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.

Usage-based device naming and 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 security-based device control are disclosed. For example, a presence-based trigger event is detected and one or more suggestion models are utilized to determine whether a security-based device control suggestion should be sent. Multiple suggestions are arbitrated, and a user device is selected for receiving the suggestion based on multiple inputs. The format of the suggestion is also determined and the suggestion is either automatically output on the selected device or a notification of the suggestion is provided.

State determination and device control

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

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.

Predictive feature analysis

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

Growing community of inventors

Seattle, WA, United States of America

William Evan Welbourne