Amazon Technologies, Inc.

Thomas F Dimson

Tom Killalea

Janna Hamaker

Joshua M Goodspeed

Eugene Kalenkovich

Todd H Sullivan

A body of text may be compared with one or more user-selected text portions to rank a plurality of text portions of the body of text, such as for predicting which of the text portions are likely to be annotated by users. As one example, the text of a content item may be compared with excerpts of other content items that have been highlighted or otherwise annotated by a plurality of users. Based at least in part on the comparison, some implementations identify one or more portions of text of the content item that are likely to be selected or highlighted by users that access the content item. In some examples, a classifier may be trained based on popular highlights determined for a plurality of content items. The classifier may be applied to a body of text to determine portions that users are likely to consider profound or interesting.

Identifying text predicted to be of interest

In some implementations, a user of an electronic device may select a term of one or more words from the text of a displayed digital work. In response, a dictionary module may select a particular dictionary to access from among a plurality of dictionaries available on the electronic device. For example, based at least in part on metadata corresponding to the displayed digital work, the dictionary module may select a dictionary to access from a hierarchy of dictionaries. The dictionary module may present a definition obtained from the selected dictionary in a dictionary interface displayed over the presentation of the digital work. Further, the dictionary module may identify a location of at least one other occurrence of the selected term in the digital work or in a different digital work, and may display an excerpt of text including the at least one other occurrence of the selected term.

Dynamic dictionary based on context

Highlights from a community of users highlighting portions of an electronic digital work may produce aggregated highlights. Users may have access to public and purchase versions of the aggregated highlights. Reconciliation of highlights across multiple versions of the same digital work may also take place, with the reconciled highlights incorporated into the aggregated highlights.

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Seattle, WA, United States of America

Thomas F Dimson