Identifying text predicted to be of interest

Patent metadata

Field	Value
Publication number	US-9852215-B1
Application number	US-201213624628-A
Country	US
Kind code	B1
Filing date	Sep 21, 2012
Priority date	Sep 21, 2012
Publication date	Dec 26, 2017
Grant date	Dec 26, 2017

Abstract

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.

First claim

What is claimed is:

1. One or more computer-readable media maintaining instructions, which when executed by one or more processors, cause the one or more processors to perform operations comprising:
accessing training data, the training data comprising:
a first text portion from a first electronic book, the first text portion associated with a positive feedback through a first user interaction received by a first computing device associated with a first user, and
a second text portion from a second electronic book, the second text portion associated with a negative feedback through a second user interaction received by a second computing device associated with a second user;

training a classifier based at least in part on the training data;
applying the classifier to a text of a third electronic book, wherein the classifier:
assigns, to a third text portion of the text of the third electronic book and independent of annotation data associated with the third electronic book, a first score that indicates a probability that the third text portion will be annotated by future users,
assigns, to a fourth text portion of the text of the third electronic book and independent of annotation data associated with the third electronic book, a second score indicating a probability that the fourth text portion will be annotated by future users,
wherein the first score and the second score are assigned based at least in part on the positive feedback received through the first user interaction, the negative feedback received through the second user interaction, and at least one of:
a similarity to a sentence structure of the at least one of the first text portion or the second text portion, or
a similarity to at least one of a type of words used in the first text portion or a type of words used in the second text portion; and

determines a ranking of at least the third text portion and the fourth text portion of the third electronic book based at least in part on the first score and the second score; and

selecting at least one of the third text portion or the fourth text portion based at least in part on the ranking.

2. The one or more computer-readable media as recited in claim 1 , wherein the ranking of the at least the third text portion and the fourth text portion of the third electronic book is based at least in part on whether individual text portions of the third electronic book are associated with at least one of:
a literary character mentioned in the third electronic book;
a person mentioned in the third electronic book;
a topic mentioned in the third electronic book;
an organization mentioned in the third electronic book;
a place mentioned in the third electronic book;
a thing mentioned in the third electronic book; or
a period of a setting mentioned in the third electronic book.

3. The one or more computer-readable media as recited in claim 1 , wherein at least one text portion of the at least the third text portion and the fourth text portion of the third electronic book is a highest-ranked text portion of the at least the third text portion and the fourth text portion of the third electronic book, the operations further comprising including the highest ranked text portion in an interface offering access to the third electronic book.

4. The one or more computer-readable media as recited in claim 1 , wherein at least one text portion of the at least the third text portion and the fourth text portion of the third electronic book is a highest-ranked text portion of the at least the third text portion and the fourth text portion of the third electronic book, the operations further comprising:
including an indication of the highest-ranked text portion in metadata associated with the third electronic book; and
sending the metadata to a user device to identify the highest-ranked text portion of the third electronic book.

5. The one or more computer-readable media as recited in claim 1 , wherein the first text portion has been further identified based at least in part on:
receiving annotation information from a plurality of respective electronic devices corresponding to a plurality of users; and
for the first electronic book, based at least in part on the annotation information, determining that the first text portion has been annotated by the plurality of users more frequently than one or more other portions of text of the first electronic book.

6. A method comprising:
under control of one or more processors configured with executable instructions, receiving a content item comprising a first body of text, the first body of text comprising at least a first text portion and a second text portion;
training a classifier based at least in part on an annotated text portion of a second body of text, the annotated text portion having been associated with a first reason through a user interaction received by a computing device associated with a first user, wherein the first body of text is different from the second body of text, and wherein, once trained, the classifier is configured to assign scores indicating a probability that a corresponding portion of the first text portion will be annotated by a second user based on the annotated text portion of the second body of text;
assigning, using the trained classifier, and to the first text portion, a first score that indicates the probability that the first text portion will be annotated by the second user;
assigning, using the trained classifier, and to the second text portion, a second score that indicates the probability that the second text portion will be annotated by the second user, wherein the first score and the second score are assigned based at least in part on the annotated text portion;
ranking, based at least in part on the first score and the second score, the at least the first text portion and the second text portion of the first body of text;
And selecting at least one of the first text portion or the second text portion based at least in part on the raking.

7. The method as recited in claim 6 , wherein the annotated text portion includes a portion of content that has been annotated by a plurality of users.

8. The method as recited in claim 7 , wherein the portion of content has been annotated by at least one of:
highlighting the text portion;
writing a note associated with the text portion;
bookmarking the text portion; or
commenting on the text portion.

9. The method as recited in claim 6 , wherein the annotated text portion is a portion of content that has been selected by the first user for posting to at least one of:
a social network site;
a microblog site; or
an online forum.

10. The method as recited in claim 6 , wherein the first body of text is a user review of a plurality of user reviews, and the operations further include presenting the first body of text to the second user based at least in part on the first score.

11. The method as recited in claim 6 , wherein the first body of text is a forum comment of a plurality of forum comments, and the operations further include presenting the first body of text to the second user based at least in part on the first score.

12. The method as recited in claim 6 , wherein the annotated text portion includes a plurality of user-selected text portions, individual user-selected portions corresponding to an individual content item, the method further comprising:
comparing the at least the first text portion and the second text portion of the first body of text with the plurality of the user-selected text portions; and
ranking the at least the first text portion and the second text portion of the first body of text based at least in par

Assignees

• Sullivan Todd H
• Dimson Thomas F
• Amazon Tech Inc

Inventors

• Sullivan Todd H
• Dimson Thomas F

Classifications

• G06F17/30731 Primary

  Physics · mapped topic

• G06F16/3334

  Selection or weighting of terms from queries, including natural language queries · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06F16/335

  Filtering based on additional data, e.g. user or group profiles (filtering in web context G06F16/9535, G06F16/9536) · CPC title

• G06F16/36 Primary

  Creation of semantic tools, e.g. ontology or thesauri · CPC title