Amazon Technologies, Inc.

Aaron James Dykstra

James Connelly Petts

Laura Ellen Grit

George M Ionkov

Samuel A Minter

Dennis H Harding

Lindsey Christina Fowler

David Michael Hurley

Stephen Brent Ivie

Yong Xi

Saurabh Nangia

Diwakar Chakravarthy

Ashutosh Vishwas Kulkarni

Adam James Finkelstein

Siyang Dai

Scott James McKee

Adam Landry Bordelon

Jesper Mikael Johansson

Tom Killalea

George Nikolaos Stathakopoulos

Adam Lloyd Days

Jinesh Udaykumar Vora

Tri M Cao

Stephen Michael Brozovich

Social network postings, including text, images or other media, may provide valuable information regarding a user of the social network with which the postings may be associated. With the authorization of the user, and upon authentication by the social network, an online marketplace may access the social network postings and extract data therefrom, and market one or more recommended items to the user based on the extracted data, which may include color pallets or texture pallets derived from photographs included in the postings.

Social media data matching

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 multi-level approach for generating suggestions and/or recommendations associated with one or more items is provided. A group of related items may be identified, for example, by generating a collaborative filtering graph or other graph that includes items and connections between the items. Description data associated with items included in the group may be evaluated in order to determine respective keywords associated with the items and/or a list of common keywords representative of the group of items or a subset of the group of items. Based at least in part upon the list of common keywords, at least one suggestion may be generated.

Clustering and recommending items based upon keyword analysis

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

A method of categorizing data points is described which, when combined with a clustering algorithm, provides groupings of data points that have an improved confidence interval. The method can be used to find an optimal number of groupings for a dataset, which in turn allows a user to categorize a group of data points for processing. In some examples, a dataset containing a number of data points may be accessed. Additionally, in some aspects, groupings of data points within the dataset may be grouped based at least in part on similarities between the data. Further, a number of groupings of data points may be adjusted so that the distance between the data points within one or more groupings of data points may fit within a confidence level.

Centroid detection for clustering

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 computing device monitors the energy level of a rechargeable battery, from which the device draws operating power. Historic usage data is used to estimate the time remaining in a present operating period, such as a workday, as well as to estimate the battery level required to provide power during that period of time. A user is then presented with information regarding the status of the battery, as well as charging time and required battery level estimates. Information regarding charging stations within publically or otherwise user-accessible venues near to the present location of the computing device is accessed and presented to the user. Reservations may be made for a charging port within a selected venue, an order may be placed for goods or services available at that venue, or other actions may be performed.

Device charging system

Disclosed are various embodiments for generating recommendations based at least in part on keywords associated with users. In some embodiments, among others, a system includes at least one computing device and a recommendation generator executable in the at least one computing device. The recommendation generator comprises logic that generates a plurality of pools of keywords based at least in part on a plurality of behavioral histories. Each pool corresponds to a behavioral history of a user across a plurality of domains. The recommendation generator also comprises logic that clusters at least a number of the keywords in a cluster across at least two pools including the same keyword and logic that recommends an item based at least in part on the cluster of keywords.

Customer keyword-based item recommendations

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

User content access events pertaining to a content item, such as an eBook, audio, video file, and so on, are collected and analyzed to determine progress data, including completion information about when the content item, or a portion thereof, has been completed. Once determined, further information or recommendations that have some relation to the completed content item may be presented to the user.

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Federal Way, WA, United States of America

Aaron James Dykstra