Amazon Technologies, Inc.

Chris Broaddus

Hue Tuan Thi

Chuhang Zou

Xiaoxia Shi

Gerard Guy Medioni

Jayakrishnan Kumar Eledath

Ejaz Ahmed

Jean Laurent Guigues

Behjat Siddiquie

Jiajun Zhao

Tian Lan

Hui Liang

Longlong Zhu

Austen Groener

Abhay Mittal

Yuzhou Feng

Users may enter and exit a facility as individuals, or as groups in which an authorized user sponsors other users. Within the group, actions within the facility taken by those sponsored users may be associated with the account of the authorized user. Sensor data acquired at a portal area is used to determine information about users passing through a portal, such as a crossing time, a distance from a crossing point, and so forth. This information is used to determine group associations. These group associations are resilient to situations, such as employees who have exited and re-entered with a user, employees holding open a gate for a user, people entering ahead of the authorized user, people entering after the authorized user, people exiting before an authorized user, and so forth.

System to determine user groupings at a portal

Cameras having storage fixtures within their fields of view are programmed to capture images and process clips of the images to generate sets of features representing product spaces and actors depicted within such images, and to classify the clips as depicting or not depicting a shopping event. Where consecutive clips are determined to depict a shopping event, features of such clips are combined into a sequence and transferred, along with classifications of the clips and a start time and end time of the shopping event, to a multi-camera system. A shopping hypothesis is generated based on such sequences of features received from cameras, along with information regarding items detected within the hands of such actors, to determine a summary of shopping activity by an actor, and to update a record of items associated with the actor accordingly.

Detecting interactions from features determined from sequences of images captured using one or more cameras

This disclosure describes, in part, systems for enabling physical retail stores and other facilities to implement both automated- and manual-checkout techniques for customers of the stores and/or facilities. For example, the described systems may enable a retail store to implement technology where users are able to pick items from shelves and other inventory locations and exit the store without performing manual checkout of the items, as well as technology to allow users to pay for their items using point-of-sale (POS) and/or other manual-checkout techniques. The systems described herein also generate associations between users and carts within the stores to prevent fraudulent transactions within these hybrid environments.

Associating carts and users in hybrid environments

This disclosure describes techniques for determining product volumes associated with products in a facility. These product volumes may be used to update planogram data associated with a facility, with the planogram data indicating inventory locations within the facility for various types of items supported by product fixtures. The planogram data may be used, in some instances, to update virtual carts of users interacting with the items in the facility.

Image-based detection of planogram product spaces

Growing community of inventors

Sammamish, WA, United States of America

Chris Broaddus