Amazon Technologies, Inc.

Other

Michel Leonard Goldstein

Ramanathan Palaniappan

Ohil Krishnamurthy Manyam

Walter Manching Tseng

Sudarshan Narasimha Raghavan

Xiaofeng Ren

Navid Shiee

Fan Sun

Dilip Kumar

Jon Robert Ducrou

Raymond Wheekyun Lim

Gerard Guy Medioni

Blair Livingstone Hotchkies

Gopi Prashanth Gopal

Sarah Graham Williams

Brent Mills

Michael Lee Brundage

Liefeng Bo

Daniel R Parshall

Jason P Patrikios

Joseph Xavier

Alexandru I Indrei

Paul Daniel Jaye

Sachin Birendra Singh

Max Lee Kanter

John Michael Nilles

Sean Phillip Forde

David Charles Couvrette

Carl N Meister

Daniel Buchmueller

Yasser Baseer Asmi

Avishkar Misra

Long Xuan Nguyen

Christopher Robert Towers

Martin Robert Frank

Sikandar Saifullah

Randall Winston Puttick

Anamika Sinha

Janna Hamaker

Ammar Chinoy

Christopher Andrew Stephens

Ismael Medrano

Adam J Iser

Vuong Van Le

Elie Micah Kornfield

Ankur Jain

Ravi Shankar Thangavel

Sikander Saifullah

Martin R Frank

This disclosure describes a system for automatically updating item image information stored in an item images data store and used for processing captured images to identify items represented in those images. In one implementation, once an identity of an item has been verified, captured images of that item are associated with the item and stored in the item images data store. As a result, the item images data store is updated each time an image of the item is captured and the identity of the item is verified.

Automated and periodic updating of item images data store

This disclosure describes a system for determining an estimated user departure time from a materials handling facility. For example, a user may enter a materials handling facility to pick one or more items from the materials handling facility. Those items may be provided to an agent for processing, such as packing, while the user picks other items within the materials handling facility. To ensure that the items are processed in a timely manner and made available to the user when the user is ready to depart from the materials handling facility, the implementations discussed determine an anticipated user departure time and compare that time with an estimated processing time needed to process the items.

Item tracking within a facility

Described is a method for determining when to transition devices between different states based on determined potential activity in an area near the devices. When the potential for activity near an input device is below a threshold, the device is maintained in an inactive state. When the potential for activity near the device exceeds the threshold, the device is transitioned to an active state before the potential activity can occur near the device. Likewise, when the device is in an active state, the implementations described herein provide the ability to determine when to begin and end transmission of data from the device to a remote computing resource for processing. For example, obtained video may be processed locally to determine if an activity is occurring. If an activity is occurring, relevant data is sent to a remote computing resource to determine if an action has been performed during the activity.

Selective transmission of data

An event at a shelf, such as a user interacting with items on the shelf, may be detected using sensors associated with the shelf. Hypotheses to describe the event are generated based on the collected sensor data. However, hypotheses determined based on only one type of sensor data may not have a confidence value that is sufficiently reliable. Hypotheses derived from different sensor types may be combined to generate new hypotheses that have higher confidence values. For example, hypotheses based on image data may be combined with hypotheses based on weight data to produce hypotheses with higher confidence values that are sufficiently reliable. A hypothesis, from the set of combined hypotheses, having the highest confidence value may then be used to describe the interaction at the shelf, such as identifying the item and quantity of item the user interacted with during the event.

Sensor data fusion for increased reliability

This disclosure describes a system for automatically identifying an item from among a variation of items of a same type. For example, an image may be processed and resulting item image information compared with stored item image information to determine a type of item represented in the image. If the matching stored item image information is part of a cluster, the item image information may then be compared with distinctive features associated with stored item image information of the cluster to determine the variation of the item represented in the received image.

Item identification among a variant of items

Items may be stowed in an inventory location, such as a shelf. An interaction may take place where one of the items is picked from or placed on the inventory location. Sensor data can be acquired from two or more sensors, such as cameras or weight sensors, configured to collect such sensor data for the inventory location. Hypotheses to describe interactions at the inventory location may be determined using sensor data from each different sensor. However, the confidence value for such hypotheses may not be reliable. Fusion of sensor data may be performed where sets of hypotheses from the different sensors are combined and the resulting hypotheses are more accurate.

Fusion of sensor data for detecting interactions at an inventory location

An item stowed at an inventory location may rest or be supported by a surface, such as a shelf, that includes an optical sensor array. The optical sensor array comprises a plurality of optical sensors that detect light intensity values. When a light source illuminates the item resting on the surface, a footprint or shadow is cast by the item onto the surface. Such a shadow is detected by the light intensity values at the optical sensors in the optical sensor array. Once the shadow is detected, information about the area, shape, and perimeter of the shadow can be determined and saved as footprint data for a single item. Such a process can be performed at intake of the item to the facility and later accessed to determine a quantity of the item on a shelf of a facility.

Generation of footprint data for items

Described is a device notification aggregation service ('DNAS') that aggregates device state information, such as alarms, from multiple distributed devices and then provides device state information and/or device set status information to services that rely on data from the distributed devices to make decisions on other events.

Device notification and aggregation

Device transitions between inactive and active states

Described is a system and method for assisting users in properly placing and/or positioning items at inventory locations within a materials handling facility. In one example, a placement location may be distinguished for the user through use of illumination or other techniques to assist the user in quickly identifying the proper location at which to place the item. Likewise, a proper position of the item may be presented to the user to assist the user in properly positioning the item at the placement location.

Item replacement assistance

This disclosure describes a system for identifying users and/or their companions, utilizing the behavior and gestures of a user's companion to assist the user, and developing user paths based on user and/or companion preferences. A companion profile may be established to identify the companion and associated the companion with the user. Likewise, a user pattern that includes both the user and the companion may be established and used to track the location of the user and/or companion while in the materials handling facility.

Identifying companion gestures and behavior

Item variation management

An item stowed at an inventory location may rest on a surface such as a shelf or may be supported above the surface. The item may exhibit a footprint that is representative of a physical perimeter of the item. For example, the footprint may be an edge of a shadow cast by the item. Information about a size and shape of a footprint associated with a particular item may be stored. An optical sensor array may acquire sensor image data indicative of the footprint of one or more of the items stored at an inventory location. A quantity of items at the inventory location may be determined by comparing the sensor image data with the stored data.

Optical item management system

Item identification among item variations

Growing community of inventors

Seattle, WA, United States of America

Michel Leonard Goldstein