Amazon Technologies, Inc.

Alan Kipust

Pragyana K Mishra

Debanjan Mukherjee

David R Azari

Thomas Matthew Ryle

Chinmay Hemant Bhagwat

Lee M Miller

Raghuram Satyanarayana Rao

Tanvi Milind Bhadbhade

Maksym Kovalenko

Denis V Batalov

Jonathan D Sanford

Anthony C Martinelli

Kelly Watson

Cynthia A Prentice

Theresa M Hollis

Techniques for managing notifications may be described. In an example, the notifications may relate to an item and may be provided to a user device. A passive device may be associated with the item. A delivery management device at a particular location may detect the proximity of the passive device. The delivery management device may cause a notification to be sent to the user device based on the proximity. The notification may describe that the item may have been detected at the particular location.

Managing notifications of a delivery method based on a passive device

Techniques for managing notifications may be described. In an example, the notifications may relate to an item and may be provided to a user device. An active device may be associated with the item. The active device may store a token for communication with a local area network associated with a location. Based on the communication, a determination may be made that the item may be in proximity to the location. Corresponding notifications may be sent to the user device.

Managing notifications of a delivery method based on an active device

Some implementations include searching for and analyzing public-domain-status information about works (such as e-books) over the Internet. A computer system may search for works recently made available online that are categorized as being in the public domain. Associated metadata is analyzed to generate a confidence level regarding whether the works are in the public domain or protected by copyright. Based on the confidence level, decisions can be made, such as whether to make the works available for free in a particular country.

Public-domain analyzer

Systems, devices, and processes for classifying a digital item are described. In some examples, a first classifier determines whether a digital item, such as an electronic book (eBook), includes content of a first category that is acceptable for publication by a publisher. A second classifier determines whether the digital item includes content of a second category that is acceptable for publication by a publisher. In response to determining that the digital item includes content of the first category or content of the second category, a third classifier may determine whether the digital item includes a phrase that is indicative of content of a third category that is unacceptable for publication.

Classifier for classifying digital items

When a digital item is submitted for publication, an automated system may determine whether the digital item includes content from other digital items. In some implementations, when the digital item is an electronic book (eBook), the automated system may select sets of words from the eBook and compute hash codes, such that each hash code corresponds to a set of words. The automated system may compare the computed hash codes with retained hash codes associated with other electronic books to determine whether the digital item includes duplicate content.

Detecting duplicated content among digital items

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Mercer Island, WA, United States of America

Alan Kipust