Amazon Technologies, Inc.

Facebook, Inc.

Ramanathan Palaniappan

Michel Leonard Goldstein

Ohil Krishnamurthy Manyam

Gopi Prashanth Gopal

Navid Shiee

Sudarshan Narasimha Raghavan

Fan Sun

Yasser Baseer Asmi

Simon Kurt Johnston

Avishkar Misra

Long Xuan Nguyen

Dilip Kumar

James Kason Keiger

Raymond Wheekyun Lim

Gerard Guy Medioni

Jon Robert Ducrou

Suresh Kumar

Xiaofeng Ren

Michael Lee Brundage

Liefeng Bo

Li Ju

Joseph Xavier

Patrick G Franklin

Ares Sakamoto

Nadya Dhalla

Zheyin Li

Sudheer Babu Ramoji

Pawel Bara

Kamil Jiwa

Matthew Bryan Folz

Jonathan Ma

Alan M Steele

Daniel Buchmueller

Christopher Robert Towers

Kun Qian

Jack G Song

Anamika Sinha

Gideon Shavit

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 an item drop location for placing items that have been picked from an inventory location within a material handling facility but have not yet been transitioned from the materials handling facility and a return location for returning items that have been transitioned from the materials handling facility. Likewise, described is a system and method for identifying an item placed at a drop location or an item placed at a return location, processing the placed item and providing confirmation to a user that placed the item and facilitating a return of the item or a removal of the item from an item identifier list associated with the user.

Inventory item release apparatus and method

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

Techniques are described for providing clients with access to events occurring during execution of defined workflows, such as under the control of a event monitoring service that is available to multiple subscribers, such as over one or more public networks. A configurable workflow service may automatically create events when a defined workflow executes, and, provide the events to the event monitoring service, which may respond to the generated events by taking various actions, such as providing event notifications, initiating workflow instances, etc.

Event-based composition model for workflow systems

An inventory location, such as a shelf, may be used to stow different types of items in different areas of the shelf. Interactions may take place, such as the pick or place of items from the shelf. Weight data acquired from weight sensors at the shelf may be used to generate a set of hypotheses to describe types and quantities of items added to or removed from the shelf during an interaction. One or more of these hypotheses may be eliminated from consideration or selected as a solution based on activity data related to the shelf. The activity data is based on non-weight data from a non-weight sensor associated with the shelf. As an example, image data from a camera directed to the shelf can help identify presence of a user or a change in appearance at an area of the shelf to help evaluate hypotheses.

Evaluation of weight hypotheses using non-weight data

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

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

An online system predicts the user's activity on the online system during a future time interval (e.g., the next day). The system collects activity data, such as actions that the user has taken on the system. The system predicts whether the user is likely to be active during the future time interval based on features extracted from the activity data. This system determines selection of notifications and delivery of notifications based on the predicted time when the user is likely to be active on the online system. The system further records the user's past interactions with notifications, such as whether the user viewed the notification, whether the user interacted with a content item associated with the notification, and so on. If system determines a rate of delivery of notifications to the user based on the frequency of past user interactions with notifications.

Adjusting pacing of notifications based on interactions with previous notifications

An inventory location such as a shelf may be used to stow different types of items. Interactions may take place, such as the pick or place of one or more items from the inventory location. Weight data may be acquired from weight sensors coupled to the shelf. A measured change in weight is used to determine a first group of hypotheses. Each of the hypotheses may be descriptive of a different permutation of products being added to or removed from the shelf that could account for the measured change in weight. The weight data is also processed to determine a weight distribution of the items on the shelf. Information about the weight distribution may be used to select a subset of the first group of hypotheses. The subset may be used to determine what items were interacted with, quantity involved, and so forth.

Item determination based on weight data

Growing community of inventors

Issaquah, WA, United States of America

Ramanathan Palaniappan