Amazon Technologies, Inc.

Timothy R Heithaus

Andrew Michael Nienhaus

Neven Tomislav Boric Bargetto

Jason Stephen Merron

Vanessa Graham Murdock

Ruimin Zhang

Sandeep Avula

Guang Liu

Sina Khaleghi

Matthew Charles Johnson

Systems, devices, and methods are provided for training and/or inferencing using a list ordering model. A list ordering model, such as a Markov chain, may be trained using a plurality of lists with location metadata in common, wherein a first list of the plurality comprises items and corresponding competition timestamps. A list ordering model may accept, as an input, a plurality of items, and determine, as an output, an order for the plurality of items. Recommendations may be generated based on the determined order.

In-store navigation using list ordering models

Techniques described herein include systems and methods for utilizing a shopping list generated by a user in one or more shopping list features. A list of items including keywords not associated with specific items may be received. A user interface may be updated to present the list of items, with a first area of the user interface corresponding to uncompleted tasks associated with a keyword of the list, and a second area corresponding to completed tasks associated with a keyword of the list. One or more sensors may receive information about an item. Input about an interaction with the user interface and a keyword of the list may be received. The user interface may be updated to move the keyword of the list from the first area to the second area. A mapping of the keyword to the item may be updated by a computer system.

Shopping list and cart integration

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Snoqualmie, WA, United States of America

Timothy R Heithaus