Amazon Technologies, Inc.

Jonathan Evan Cohn

Jeremy Samuel De Bonet

Oded Maron

Michael James O'Connor

Emmanuel Jm Laffon De Mazieres

Alexsandra M Bowers

Paul Douglas Grearson

Youssef Takhchi

Ryan David Hruska

Nicholas McMahon

Xiang Ma

Heather Gleason

Andrew Stubbs

Thomas Burns

Ejaz Ahmed

Jacob Alex Siegel

Mirko Ristivojevic

Matthew Clark Webster

Daniel Ron

Robert P Irwin

David Wai-Man Wong

Charles H Wood

Marcus Townsend

James Siminoff

Treasure Hinds

Greg Janky

Alexandra Specht

Ryan Andrew Crawford

Aaron Timmons

Justin Brousseau

Victoria Hunt

Jon Varteresian

Sulaiman Kazi

Emmanuel Laffon Laffon De Maziere

Battery expansion pack

Camera case

Camera

Floodlight

This disclosure is directed to using cascading algorithms to automatically identify items placed in a tote or other receptacle utilized by users in material handling facilities as the users move around the facilities. A tote may store a database or 'gallery' of item representations for all of the items that are stored in the facility that a user may place in their totes. The tote may use multiple algorithms in a cascading manner to analyze the gallery of item representations in order to iteratively narrow the search space of item representations in the gallery to determine which of the items was placed in the tote by a user. Upon identifying the item placed in the tote, the tote may add an item identifier for the item to a virtual listing of item identifiers representing items previously placed in the tote.

Identifying items using cascading algorithms

This disclosure is directed to item-identifying carts that may be utilized by users to automatically identify items that the users place in their carts. In addition, these carts may automatically determine the outcome of respective events that occur with respect to these identified items. For example, the carts may be configured to identify one or more items that are placed into or removed from the cart, and thereafter determine one or more actions taken with respect to the identified items and a quantity of the items involved. For example, after identifying a first item and a second item either placed into or removed from the cart, the cart may determine that the user added two instances of the first item and removed one instance of the second item. In response to making this determination, the cart may update a virtual cart of a user operating the physical cart.

Resolving events in item-identifying carts

This disclosure is directed to an item-identifying, mobile cart that may be utilized by a user in a materials handling facility to automatically identify a user operating the cart and items that the user places into a basket of the cart. In addition, the cart may update a virtual shopping cart of the identified user to include items taken by the user. The mobile cart may include multiple imaging devices and oriented such that their respective optical axes are directed towards an interior of a perimeter of the top of the basket, and above the top of the basket. The mobile cart may also include an imaging device oriented away from the basket such that a user operating the mobile cart may scan a user identifier using this imaging device to enable recognition of the user.

Item-identifying carts

Method and apparatus for evaluating user movement in a fulfillment center. A plurality of sub-tasks for performing a fulfillment operation are determined. Embodiments retrieve a training set of motion data that includes a plurality of training motion data samples, each specifying motion data over a respective window of time, and train a first data model to classify instances of motion data into portions, based on the plurality of sub-tasks. At least one data model is trained to assess performance of the fulfillment operation, using the training set of motion data. An instance of motion data describing motion performed during the fulfillment operation is received, the instance of motion data is divided into a plurality of portions, using the first data model, and a measure of quality is generated, by analyzing the plurality of portions of the instance of motion data using the trained at least one data model.

Motion data driven performance evaluation and training

This disclosure is directed to an item-identifying cart that may be utilized by a user in a materials handling facility to automatically identify items that the user places in their cart, and update a virtual shopping cart to include items taken by the user. The mobile cart may include four capture assemblies that are disposed proximate to each of the four corners of a basket of the cart, and oriented such that their respective optical axes are directed towards an interior of a perimeter of the top of the basket, and above the top of the basket. The capture assemblies may include proximity sensors that are used to detect movement above the top of the basket, LEDs that illuminate items, and cameras that generate image data representing the items as they are placed in, or removed from, the cart.

Growing community of inventors

Winchester, MA, United States of America

Jonathan Evan Cohn