Amazon Technologies, Inc.

Paul Aksenti Savastinuk

Ross David Roessler

William Evan Welbourne

Jim Oommen Thomas

Cheng-Hao Kuo

Yinfei Yang

Geoffrey Heller

Michael Dillon

Michael Lee Sandige

Tsz Ho Yu

Kritarth Jain

Gopi Prashanth Gopal

Roman Talyansky

Hongyang Wang

Eli Osherovich

Sunny Singh

Marc Wetter

Chandra Prakash Konkimalla

Yishuai Li

Charles Winn Degranville

Louis LeRoi LeGrand, Iii

Rohith Mysore Vijaya Kumar

Timothy Andrew Ong

Paul Eugene Munger

Matthew Alan Townsend

Kah Kuen Fu

Charles Edwin Ashton Brett

Sara Parker Hillenmeyer

George Strajan

Amit Dekate

Steven Scott Noble

Deon Poncini

Geoff Hunter Donaldson

Joseph James Greene

Franklin Munoz Garcia

Amir Salimi

Scott Brown

Matthew Scott Wilding

Meet Prakash Vadera

Siyuan Liu

Arpit Jain

Rajesh Bangaru Ravindranath

Shirish R Nair

Steven Xiang Chen

Xiaodong Tian

Jagrut Arora

Christina Alexandra Polyukh

Michal Marek Kozlowski

Andrew Bartlett Leonard

Geoffrey Scott Heller

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

This disclosure describes systems and techniques for detecting events, determining a result of each respective event using a first hypothesis source, and calculating a likelihood that a second (and/or additional) hypothesis source would determine the same result of the respective event. The calculated likelihood may then be used to be determine whether to request that the second hypothesis source determine the result of the event, determine an amount of resources of the second hypothesis source to use to make this determination, and/or like.

Generating event results using auto-generated sensor data

Systems and techniques for detecting events involving a user that occurred during a session in a facility, and performing a holistic, session-based analysis of the events to determine items taken by the user from the facility. An inventory management system may initially analyze the events to determine a hypothesis for a result of each respective event and a probability that the hypotheses are accurate. An initial listing of the items determined to be taken by the user may be generated along with probabilities that the items were taken. Upon determining that a probability an item was taken is lower than a threshold, the management system may select an algorithm to process an event in the session that is likely to resolve uncertainty in the initial listing. The management system may iteratively process events until a final listing represents the items taken by the user with an allowable probability.

Session-based analysis of events

Systems and techniques for detecting events involving users that occurred at an inventory location, and then using the events to determines states of the inventory location at various times. For instance, a system may generate event data representing first events that occur within an inventory location. The first events may include an associate performing a task, a customer removing an item, a customer returning an item, and/or so forth. The system may then use the event data to determine a state of the inventory location during a second event. In some examples, the state may include a tidy state or an untidy state. If the state includes a tidy state, then the system may generate a listing of items that includes at least one item associated with the second event. However, if the state includes an untidy state, then the system may further process the second event.

Analysis of events for state detection

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.

User identification based on voice and face

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&#xb0; 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.

Panoramic image generation from video

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&#xb0; 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.

Remote immersive user experience from panoramic video

Systems and methods may be directed to de-blurring panoramic images and/or video. An image processor may receive a frame, where the frame comprises a plurality of pixel values arranged in a grid. The image processor may divide the frame into a first section and a second section. The image processor may determine a first motion kernel for the first section and apply the first motion kernel to the first section. The image processor may also determine a second motion kernel for the second section and apply the second motion kernel to the second section.

Motion de-blurring for panoramic frames

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&#xb0; video. The remote computing resources may apply one or more algorithms to the stitched video data to adjust the color characteristics of the stitched video data, such as lighting, exposure, white balance contrast, and saturation. The remote computing resources may further smooth the transition between the video data captured by the multiple cameras to reduce artifacts such as abrupt changes in color as a result of the individual cameras of the video capture device having different video capture settings. The video capture device and/or the remote computing resources may generate a panoramic video that may include up to a 360&#xb0; field of view.

Color adjustment of stitched panoramic video

The hand which a user is using to hold an electronic device can be determined by analyzing data captured by one or more motion sensors on the device. The curvature to the motion can be indicative of handedness, and processing motion features using a classifier algorithm can enable the determination of handedness with a corresponding confidence. In some embodiments, motion data is collected over a monitoring window, and handedness values are accepted when the handedness value remains the same with at least a minimum confidence for at least a minimum number of window periods. A determination of handedness enables an operating system and/or applications executing on the device to adjust one or more operational or interface aspects in order to make it easier for the user to operate the device using the hand currently holding the device.

Handedness determinations for electronic devices

Devices, systems and methods are disclosed for identifying content in video data and creating content-based zooming and panning effects to emphasize the content. Contents may be detected and analyzed in the video data using computer vision, machine learning algorithms or specified through a user interface. Panning and zooming controls may be associated with the contents, panning or zooming based on a location and size of content within the video data. The device may determine a number of pixels associated with content and may frame the content to be a certain percentage of the edited video data, such as a close-up shot where a subject is displayed as 50% of the viewing frame. The device may identify an event of interest, may determine multiple frames associated with the event of interest and may pan and zoom between the multiple frames based on a size/location of the content within the multiple frames.

Content-based zooming and panning for video curation

A tracking architecture is provided that enables data for gestures and head positions to be provided to both native and non-native clients on a computing device. A pipeline component can obtain the raw image data and sensor data and synchronize that data to be processed to determine, for example, location and/or motion data that may correspond to device input. The data can be processed by separate components, such as an event publisher and an event provider, each capable of filtering the location, motion, and/or raw sensor data to generate a set of event data. The event data then can be published to registered listeners or provided in response to polling requests. Head coordinates, gesture data, and other such information can be passed through one or more interface layers enabling the data to be processed by a non-native client on the device.

Architectures for processing of head tracking on a mobile device

Growing community of inventors

Shoreline, WA, United States of America

Paul Aksenti Savastinuk