Amazon Technologies, Inc.

Snap Inc.

Other

Daniel Ron

Oded Maron

Ejaz Ahmed

Jeremy Samuel De Bonet

Ning Xu

Shenlong Wang

Dhritiman Sagar

Sumant Milind Hanumante

Kun Duan

Chongyang Ma

Jonathan Evan Cohn

Mirko Ristivojevic

Sajjad Beygiharchegani

Michael Derryberry

Shamil Abzalov

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

An active depth detection system can generate a depth map from an image and user interaction data, such as a pair of clicks. The active depth detection system can be implemented as a recurrent neural network that can receive the user interaction data as runtime inputs after training. The active depth detection system can store the generated depth map for further processing, such as image manipulation or real-world object detection.

Active image depth prediction

This disclosure is directed to item-identifying carts that may generate and store image data of items being placed into, or removed from, the carts in the event that a cart is unable to recognize an item. For example, the cart may analyze frames of generated image data, representing item(s) being placed into or removed from the cart, to determine which one or more frames to store. If the cart is later unable to identify such an item, the cart may retrieve one or more of the stored frames, generate a user interface (UI) that includes these frame(s) and a request that a user operating the cart to help identify the item, and present the UI on a display of the cart.

Resolving events in item-identifying carts

This disclosure describes using trained classifiers and additional data to identify items placed into a tote. The techniques may use a first classifier on image data representing an item to determine a region associated with a barcode of the item and may use one or more second classifiers to analyze the region for determining a candidate barcode. The techniques may then determine whether the candidate barcode corresponds to the actual barcode of the item. If so, the techniques may update a virtual cart associated with a user account to indicate the acquisition of the item. If not, then the techniques may use the additional data for identifying the item. For example, the techniques determine a current location of a tote for determining items that are within a threshold distance of the location and may use this information, along with the candidate barcode, to identify the item placed into the tote.

Identifying item barcodes

Growing community of inventors

Lexington, MA, United States of America

Daniel Ron