The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 18, 2017

Filed:

Oct. 21, 2015
Applicant:

Qbase, Llc, Reston, VA (US);

Inventors:

Scott Lightner, Leesburg, VA (US);

Franz Weckesser, Spring Valley, OH (US);

Sanjay Boddhu, Dayton, OH (US);

Robert Flagg, Portland, ME (US);

Assignee:

QBase, LLC, Reston, VA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 15/18 (2006.01); G06N 5/02 (2006.01); G06F 17/20 (2006.01); G06N 99/00 (2010.01); G06N 5/04 (2006.01); G06F 17/27 (2006.01); G06F 17/30 (2006.01);
U.S. Cl.
CPC ...
G06N 5/022 (2013.01); G06F 17/20 (2013.01); G06F 17/2705 (2013.01); G06F 17/30011 (2013.01); G06F 17/30598 (2013.01); G06N 5/04 (2013.01); G06N 99/005 (2013.01);
Abstract

The present disclosure relates to a method for performing automated discovery of new topics from unlimited documents related to any subject domain, employing a multi-component extension of Latent Dirichlet Allocation (MC-LDA) topic models, to discover related topics in a corpus. The resulting data may contain millions of term vectors from any subject domain identifying the most distinguished co-occurring topics that users may be interested in, for periodically building new topic ID models using new content, which may be employed to compare one by one with existing model to measure the significance of changes, using term vectors differences with no correlation with a Periodic New Model, for periodic updates of automated discovery of new topics, which may be used to build a new topic ID model in-memory database to allow query-time linking on massive data-set for automated discovery of new topics.


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