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02/26/09 - USPTO Class 707 |  1 views | #20090055389 | Prev - Next | About this Page  707 rss/xml feed  monitor keywords

Ranking similar passages

USPTO Application #: 20090055389
Title: Ranking similar passages
Abstract: Passages in a digital corpus are scored and ranked based at least in part on characteristics of instances of the passages occurring in the corpus. Such characteristics include the popularity of the author, the characteristics of the words introducing and following the similar passage, frequency of appearance of the passage in the digital corpus, the length of the similar passage, the words of the similar passage, the usage of punctuation with the similar passage, and the diffusion of the similar passage within the digital corpus. The characteristics are scored and weighted to produce ranking scores for the associated passages. The ranking scores are used for purposes including selecting passages to display in association with a document and ranking passages displayed in response to a search. (end of abstract)



Agent: Google / Fenwick - Mountain View, CA, US
Inventors: William Noah Schilit, Okan Kolak, Justin John Paul Vincent-Foglesong
USPTO Applicaton #: 20090055389 - Class: 707 5 (USPTO)

Ranking similar passages description/claims


The Patent Description & Claims data below is from USPTO Patent Application 20090055389, Ranking similar passages.

Brief Patent Description - Full Patent Description - Patent Application Claims
  monitor keywords CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Patent Provisional Application No. 60/956,880, filed Aug. 20, 2007, the contents of which are hereby incorporated by reference.

This application is related to U.S. patent application Ser. No. 11/781,213, filed Jul. 20, 2007, and titled “Identifying and Linking Similar Passages in a Digital Text Corpus,” the contents of which are hereby incorporated by reference.

BACKGROUND

1. Field of Art

This invention pertains, in general, to scoring similar passages in digital text documents and, in particular, to ranking similar passages based on characteristics of the similar passages occurring in the digital text documents.

2. Description of the Related Art

Advancement in digital technology has changed the way people acquire information. For example, people can now view electronic documents that are stored in a predominantly text corpus such as a digital library that is accessible via the Internet. Such a digital text corpus is established, for example, by scanning paper copies of documents including books and newspapers, and then applying an optical character recognition (OCR) process to produce computer-readable text from the scans. The corpus can also be established by receiving documents and other texts already in machine-readable form.

Many of these electronic documents contain similar passages or quotations that appear multiple times within the corpus. Users may search for documents in the digital corpus based on various search queries. Additionally, users may search for the documents based on known or popular quotations or phrases contained in the documents. However, these types of searches may yield thousands of matching results and the most relevant results may not initially be displayed making it difficult for users to locate the documents or passages most relevant to their queries.

SUMMARY

The problems described above are addressed by a computer-implemented method, computer program product, and computer system for calculating a score for a passage having a plurality of instances occurring in a digital corpus. Embodiments of the method comprise calculating at least one score based at least in part on characteristics of instances of the passage occurring in the digital corpus and generating a ranking score associated with the passage based at least in part on the calculated at least one score. The method further comprises storing the ranking score in association with the passage in a computer-readable medium. Embodiments of the computer program product and computer system comprise computer code for performing similar functions.

The features and advantages described in the specification are not all inclusive and, in particular, many additional features and advantages will be apparent to one of ordinary skill in the art in view of the drawings, specification, and claims. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the disclosed subject matter.

BRIEF DESCRIPTION OF DRAWINGS

The disclosed embodiments have other advantages and features which will be more readily apparent from the detailed description, the appended claims, and the accompanying figures (or drawings). A brief introduction of the figures is below.

FIG. 1 shows an environment adapted to support ranking similar passages according to one embodiment.

FIG. 2 is a high-level block diagram illustrating a functional view of a typical computer for use as one of the entities illustrated in the environment of FIG. 1 according to one embodiment.

FIG. 3 is a high-level block diagram illustrating modules within the scoring engine according to one embodiment.

FIG. 4 is a flow chart illustrating steps performed by the scoring engine according to one embodiment.



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