Amazon Technologies, Inc.

Jules Cook Graybill

James Christopher Curlander

Marshall Friend Tappen

Barry James O'Brien

Wesley Scott Lauka

Ryan Scott Russell

Udit Madan

Pragyana K Mishra

Jonathan Blair Norwood

Darren Ernest Canavor

Danny Guan

Jonathan David Phillips

Michael Ellsworth Bundy

Allan Scott Bathurst

David Daniel Glick

Hilliard Bruce Siegel

Maren Marie Costa

Nigel J Green

Gus Lopez

Alexandre Edelman

Erik Resch Parker

Jonathan McGuire

Katherine M Ryan

Jeremy David Sawatzky

Kristina Lisa Klinkner

Michael Anthony Molello

Doug Treder

Peter R Wurman

Peter K Mansfield

Ned Lecky

Gregory James Nyssen

Robert Wadia Moses

Gil Blank

Gregory J Bartlett

Jill Kathleen Sestini

Systems and methods to improve video conferencing may include various improvements and modifications to image and audio data to simulate mutual presence among users. For example, automated positioning and presentation of image data of users via presentation devices may present users at a position, size, and scale to simulate mutual presence of users within a same physical space. In addition, rendering image data of users from virtual camera positions via presentation devices may simulate mutual eye gaze and mutual presence of users. Further, blending a background, or accentuating a user, within image data that is presented via presentation devices may match visual characteristics of the environment or the user to simulate mutual presence of users. Moreover, dynamic steering of individual audio transducers of an audio transducer array, and simulating audio point sources using a combination of speakers, may simulate direct communication and mutual presence of users.

Image and audio data processing to create mutual presence in a video conference

This disclosure describes a device and system for verifying the content of items in a bin within a materials handling facility. In some implementations, a bin content verification apparatus may pass by one or more bins and capture images of those bins. The images may be processed to determine whether the content included in the bins has changed since the last time images of the bins were captured. A determination may also be made as to whether a change to the bin content was expected and, if so, if the determined change corresponds with the expected change.

Bin content verification

Disclosed are various embodiments for coordination of autonomous vehicles in a roadway. A roadway management system can generate lane configurations for a roadway or a portion of the roadway. The roadway management system can determine the direction of travel for lanes in a roadway and direct autonomous automobiles to enter the roadway in a particular lane.

Lane assignments for autonomous vehicles

Inventory imaging apparatus

Systems and methods for receiving objects into inventory are described. The objects are individually scanned to generate scan data. The scan data is processed to determine what the object is (and to add the object into inventory) and also to determine values for one or more additional attributes. The attribute values are stored in a data structure which may be analyzed to detect a change over time.

Item attribute collection

An inventory system can include radio frequency identification (RFID) tags and RFID tuners that can be brought into interacting proximity with one another to provide input or other information about the location or other condition of movable elements within the inventory system. For example, interactions of RFID tags and RFID tuners at different locations within an inventory system may be utilized to associate a container or other movable object with particular receiving surfaces at particular points in time to facilitate identification of the whereabouts of the container or other movable object.

Location from RFID tag and RFID tuner interaction

Disclosed are various embodiments for obtaining environmental data and operational data from autonomous vehicles in a roadway. A vehicle state of the autonomous vehicles can be updated using data that is obtained from nearby vehicles or other vehicles that are on the roadway. The roadway management system can also generate updates to the vehicle state based upon data obtained from sources external to autonomous vehicles.

Generating state information for autonomous vehicles

Disclosed are various embodiments for non-mechanical assistance for autonomous vehicles. An unknown environmental condition may be detected that affects a navigation of the autonomous vehicle. A passenger and/or other source may be presented an inquiry requesting identification of the unknown environmental condition. A response to the inquiry may be used as an input by the vehicle control system in determining appropriate navigation of the autonomous vehicle.

Environmental condition identification assistance for autonomous vehicles

Imaging devices may be mounted to one or more hands of a worker, e.g., on a glove or other substrate that maybe worn on the hands of the worker, and used to capture images or imaging data from a perspective associated with the hands. The images or imaging data may be stored, analyzed or displayed on a computer display, e.g., a display provided on the glove or substrate. Additionally, the images or imaging data may be evaluated to determine whether a task has been properly performed by the worker, such as by identifying one or more markings (e.g., bar codes) that may be provided within the images or the imaging data.

Hand-mounted networked imaging devices

A suitable imaging device for capturing images of an object or an area of interest may be automatically selected based on information regarding the locations and/or other operational properties of a plurality of imaging devices, and a location of the object or the area of interest. One or more geometric models of the fields of view of the imaging devices may be generated, and whether the object or the area of interest falls within one or more of the fields of view may be determined using such models. Where multiple imaging devices may include the object or the area of interest, within a field of view, the imaging device having the most suitable image, e.g., the largest or highest resolution image of the object or the area of interest, may be selected.

Automatic imaging device selection for video analytics

Imaging data regarding an interior portion of a storage vessel may be obtained from an imaging device (e.g., a digital camera or depth sensor) and interpreted in order to identify an available volume within the storage vessel. Where the imaging data includes an image of an interior of the storage vessel, an area of one or more contiguous spaces of a rear face of the storage vessel may be identified from the image and multiplied by a depth of the storage vessel in order to estimate the available volume. Where the imaging data includes a depth profile of the interior, the depth profile may be processed in order to estimate the available volume. Once an available volume has been estimated, the capacity of the available volume to accommodate one or more additional items may be determined, and information regarding the capacity may be provided to a user.

Selecting items for placement into available volumes using imaging data

Visual cues may be provided for the purpose of triggering the performance of one or more actions associated with a given task. A visual cue may include one or more characters or other markings on any relevant surfaces, as well as naturally appearing edges, contours or outlines within an environment in which the visual cue is provided. When one or more visual cues appear within a field of view of an imaging device, and the visual cues are recognized as such, a task relating to the visual cues may be identified, and instructions for performing the task may be provided. The task may be dependent upon a context in which the visual cues are provided, such that a single visual cue may be associated with multiple tasks in different contexts, and the performance of the task may be confirmed based on imaging data or other relevant information.

Performing automated tasks based on visual cues

Growing community of inventors

Seattle, WA, United States of America

Jules Cook Graybill