Amazon Technologies, Inc.

Samuel A Minter

George M Ionkov

Aaron James Dykstra

James Connelly Petts

Dennis H Harding

Laura Ellen Grit

Lindsey Christina Fowler

Daniel Bruce Lloyd

Yong Xi

Michal Maciej Bryc

Brett E Lounsbury

Stephen Brent Ivie

Jeffrey B Slosberg

Tri M Cao

Joseph Steven Mate

Vishal M Shanbhag

Llewellyn James Mason

Pedrito Uriah Maynard-Zhang

Llewllyn J Mason

Michal M Brye

Systems and methods are provided for selecting components to include in portions of a displayable file. Selecting the components may include determining an order of the components for each portion of the displayable file. The components' order for a given portion may be based on a score for each component, where a component's score is based on an estimated value and standard error associated with the component. The component to include in each portion of the displayable file may be selected based at least in part on the determined component order for each portion and a predetermined priority of each portion.

Systems and methods for selecting components for inclusion in portions of a displayable file

A user device displays portions of an electronic publication for a user to read. The user device tracks the user's reading behavior of the portions of the electronic publication. The user device then suggests additional reading material for the user based on the user's reading behavior.

Reading material suggestions based on reading behavior

A customized questionnaire is generated for a content item, such as an eBook, audio file, video file, and so on. Upon an occurrence of predetermined event, the user is presented with the customized questionnaire soliciting responses to questions and/or rating evaluations relating to the content item. The responses may include reviews, ratings, recommendations of similar items, discussion topics, and other things. Information from the responses may be collected and associated with the content item to build a user-driven index.

Categorization based on user questionnaire

Content is distributed electronically to a plurality of users. As the users consume the content, they may annotate the content to indicate corrections, comments, or other information. Annotations are collected at a server and reported back to content creators, such as authors, publishers, translators, editors, etc.

Annotations for content creation feedback

Electronic content items such as electronic books are enhanced by identifying citations within the content items, identifying sources of the objects of the citations, and associating the citations with such sources so that readers of the content items can easily purchase or otherwise obtain the citation objects. The citations may be updated as new products become available or information related to the products changes over time.

eBook citation enhancement

A user device displays portions of an electronic publication for a user to read. The user device tracks the user's reading behavior of the portions of the electronic publication. The user device then selects a problem from a group of pre-generated problems for the user based on the user's reading behavior and provides the selected problem to the user.

User device providing electronic publications with dynamic exercises

Techniques for automated targeting of content components to users are described. Content components are selected and exposed through renderable pages for viewing by a population of users. User activity following exposure is tracked in an effort to identify which types of users (as characterized by certain attributes) are likely to act on the content components. The users are segmented into groups according to the attributes and the segments are fed back to aid in selection of content components to be exposed to the users. This enables more granular targeting of the content components to those users who exhibit the attributes that define the specific groups.

Automated targeting of content components

A decision tree is provided as a machine learning classifier to predict a user attribute, such as a geographical location of a user, based on a network address. More specifically, the decision tree is constructed via machine learning on a set of sample data that reflects a relationship between a network address and a user attribute of a 'known user' whose profile information is recognizable. For a given network address, the decision tree can be used as a machine learning classifier to predict the most likely user attribute of a potential user. With the predicted attribute, a network service can target a group of potential users for various campaigns without recognizing the identities of the potential users.

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Bellevue, WA, United States of America

Samuel A Minter