Amazon Technologies, Inc.

Snap Inc.

Amber Autrey Taylor

Jason Michael Famularo

Thomas Meilandt Mathiesen

Dilip Kumar

Gianna Lise Puerini

Brent Mills

Liza Marie Walsh

Korwin Jon Smith

Sridhar Boyapati

Alexander Michael McNamara

Jared Joseph Frank

David James Roy Erdmann

Daniel Thomas Mellott

Walton Lin

Andrew Lin

Leonid Gorkin

Amer Shahnawaz

Eric Wood

Lillian Zheng

Gary Alexander Campbell

Anup Prabhakar Dhalwani

Ammar Chinoy

Paul Anthony Kotas

Steven Kessel

Wyatt David Camp

Benjamin Ralph Hollis

Srikanth Thirumalai

Robert Yuji Haitani

Aneeb Qureshi

Félix Joseph Étienne Pageau

Alexandru I Indrei

Dan Catalin Teodorescu

Jean Joseph Tavernier

Alexandre Alexandrovich Klementiev

Jamie Broomall

Balaji Nageswaran

David W Law

Nassir Samatar

Shreeraj Sutaria

Michael Lee Herst

Jeffrey Clarke Purcell

Jon Darren Fleming

John Devlin Lynch

Chee H Chew

Cory William Toedebusch

Sharon Elizabeth Chiarella

Described is a system and method for presenting event information to a user and, if necessary, obtaining confirmation of different aspects (user, item, action) of the event. In some implementations, an event includes a user, an action, and an item. For example, an event may include a user picking an item from an inventory location, a user placing an item into a tote associated with the user, etc. If the aspects of the event cannot be determined with a high enough degree of confidence, a user interface may be generated and sent to the user requesting confirmation of one or more of the aspects of the event.

Event determination and presentation

This disclosure describes a system for associating multiple totes with a single profile so that items placed into any of the multiple totes are identified on a profile item identifier list. For example, if two users located in a materials handling facility are picking items that are to be consolidated or otherwise treated together, each user may select a different tote and move separately through the materials handling facility. As either user picks items and places those items into a corresponding tote, a profile item identifier list is updated to include an item identifier for each picked item.

Group shopping

Described is a system and method for proactively determining and resolving events having low confidence scores and/or resolving disputed events. For example, when an event, such as a pick of an item from an inventory location within a materials handling facility occurs, the event aspects (e.g., user involved in the event, item involved in the event, action performed) are determined based on information provided from one or more input components (e.g., camera, weight sensor). If each event aspect cannot be determined with a high degree of confidence, the event information is provided to an associate for resolution.

Event image information retention

Described is a system and method for presenting event information to a user and, if necessary, obtaining confirmation of different aspects (user, item, action) of the event. In some implementations, an event includes a user, an action, and an item. For example, an event may include a user picking an item from an inventory location, a user placing an item into a tote associated with the user, etc. if the aspects of the event cannot be determined with a high enough degree of confidence, a user interface may be generated and sent to the user requesting confirmation of one or more of the aspects of the event.

Item and action determination

Systems and methods are provided for analyzing messages generated by a plurality of computing devices associated with a plurality of users in a messaging system to generate training data to train a machine learning model to determine a probability that a media content item was generated inside an enclosed location or outside, receiving a media content item from a computing device, analyzing the media content item using the trained machine learning model to determine a probability that the media content item was generated inside an enclosed location or outside, determining, based on the probability generated by the trained machine learning model, that the media content item was generated inside an enclosed location, and determining an inside temperature associated with the venue based on messages generated by a plurality of computing devices in a messaging system comprising media content items and temperature information for the venue or a similar venue type.

Generating weather data based on messaging system activity

Tote and item association

Event aspect determination

Proactive resolution of event information

Techniques for improving access to and interactions with bots are described. In an example, a first bot, hosted on a computing system, may identify an action to be performed for a user associated with a computing device. The action may be identified based on a user interaction with the first bot, where the user interaction may be provided from the computing device. The first bot may select a second bot based on the action. The second bot may be hosted on a same or a different computing system. The first bot may determine, based on a previous user input to the first bot, a parameter value for an execution of the action and may send the parameter value to the second bot. In response, the first bot may receive a result of an execution of the action and may provide information about the result to the computing device.

Bot search and dispatch engine

Described are systems and techniques configured to determine a tote placement indicative of a position of a user relative to a tote, or vice versa. The determination may be based at least in part on data such as images acquired from cameras onboard the tote, images acquired from cameras external to the tote, proximity sensors onboard the tote, weight sensors, and so forth. Orientation of information presented on a display device of the tote may be based on the tote placement.

Determining position of a user relative to a tote

Access to items stored within an inventory location of a facility may be controlled using physical access control mechanisms. A user within a facility is detected by sensors, such as imaging sensors. Context data for the user is generated based on the sensor data. The context data includes permission data indicative of portions of the inventory location that are accessible or inaccessible to the user. In addition, a predicted path of the user may also be determined based on historical data of the user or other users of the facility. Using the permission data and the predicted path of the user, configuration data may be generated to specify the level of access control to be applied to the different portions of the inventory location. The configuration data is sent to access control mechanisms of the inventory location to prevent or allow access to such portions of the inventory location.

Controlling access to items in an inventory location

Described are systems for a materials handling facility to emit air from a floor which is used to generate an air cushion upon which one or more totes may move. The floor may contain one or more apertures which receive air from an air reservoir which is supplied by an air source such as a compressor. Air emitted by these apertures is constrained by a deck of the tote, forming an air cushion upon which the tote rests. In some implementations the air cushion may be maintained while a user is within a threshold distance, and discontinued when the user is beyond the threshold distance.

Air cushioned materials handling system

Tote association

Growing community of inventors

Seattle, WA, United States of America

Amber Autrey Taylor