Data classification methods using machine learning techniques

Details Data classification methods using machine learning techniques Data classification methods using machine learning techniques

2011-06-07

2011-06-07

Fernandez Rivas, Omar F (Department: 2129)

Data processing: artificial intelligence

Neural network

Learning task

C706S012000, C706S015000

Reexamination Certificate

active

07958067

ABSTRACT:
Methods for classifying documents are presented. Methods for analyzing documents associated with legal discovery are also presented. Methods for cleaning up data are also presented. Methods for verifying an association of an invoice with an entity are also presented. Method for managing medical records are presented. Method for face recognition are presented.

REFERENCES:
patent: 5159667 (1992-10-01), Borrey et al.
patent: 6192360 (2001-02-01), Dumais et al.
patent: 6327581 (2001-12-01), Platt
patent: 6370277 (2002-04-01), Borrey et al.
patent: 6463430 (2002-10-01), Brady et al.
patent: 6633857 (2003-10-01), Tipping
patent: 7184929 (2007-02-01), Goodman
patent: 7340376 (2008-03-01), Goodman
patent: 7366705 (2008-04-01), Zeng et al.
patent: 7562060 (2009-07-01), Sindhwani et al.
patent: 2002/0165717 (2002-11-01), Solmer et al.
patent: 2003/0101161 (2003-05-01), Ferguson et al.
patent: 2003/0120653 (2003-06-01), Brady et al.
patent: 2004/0111453 (2004-06-01), Harris et al.
patent: 2005/0134935 (2005-06-01), Schmidtler et al.
patent: 2005/0216426 (2005-09-01), Weston et al.
patent: 2005/0234955 (2005-10-01), Zeng et al.
patent: 2005/0265618 (2005-12-01), Jebara
patent: 2006/0093208 (2006-05-01), Li et al.
patent: 2006/0120609 (2006-06-01), Ivanov et al.
patent: 2006/0235812 (2006-10-01), Rifkin et al.
patent: 2007/0005341 (2007-01-01), Burges et al.
patent: 2007/0239642 (2007-10-01), Sindhwani et al.
Joachims, “Transductive Learning Via Spectral Graph Partitioning”, ICML, 2003, pp. 1-8.
Joachims, “Transductive Interference for Text Classification using Support Vector Machines”, 1999, pp. 1-10.
Jaakoba et al., “MAximum Entropy Discrimination”, 1999, pp. 1-26.
Notice of Allowance from U.S. Appl. No. 10/412,613 mailed on Mar. 25, 2008.
Office Action Summary from U.S. Appl. No. 11/752,691 mailed on Feb. 24, 2010.
Nigam, Using Unlabeled Data to Improve Text Classification, Doctoral Thesis, Carnegie Mellon University, 2001, pp. 1-128.
Office Action Summary from U.S. Appl. No. 11/752,719 mailed on Nov. 30, 2009.
Thorsten, Joachims, “Transducive Interference for Text Classification Using Support Vector Machines”, Proceedings of the International Conference on Machine Learning (ICML), 1999, pp. 1-10.
Jebara et al., “Feature Selection and Dualities in Maximum Entropy Discrimination”, 16thConference on Uncertainty in Artificial Intelligence, UAI 2000. Jul. 2000, pp. 1-10.
Chen et al., “Learning with Progressive Transductive Support Vector Machines”, 2003, pp. 1845-1855.
U.S. Appl. No. 11/752,634, filed May 23, 2007.
U.S. Appl. No. 11/752,691, filed May 23, 2007.
U.S. Appl. No. 11/752,719, filed May 23, 2007.
Jaakkola et al., “Maximum entropy discrimination” Aug. 18, 1990, Revised Oct. 10, 1999, pp. 1-26.
Office Action Summary from U.S. Appl. No. 10/412,163 mailed on Oct. 25, 2007.
“The American Heritage College Dictionary; Fourth Edition”—definition of “relationship”. 2004., p. 1173.
Rennie, J.D. et al., “Improving Multiclass Text Classification With The Support Vector Machine”. Oct. 2001., pp. 1-14.
Kolcz, A. et al., “Summarization As Feature Selection For Text Categorization” CIKM '01 Nov. 2001, pp. 365-370.
Cristianini, N. et al., “An Introduction To Support Vector Machines And Other Kemel-Based Learning Methods” pp. 8-11, 26-41, 92-101, 124-129. 2000.
Kindermann, J. et al., “Error Correcting Codes With Optimized Kullback-Leibler Distances for Text Categorization”. PKDD 2001. Springer-Verlag 2001, pp. 276-276.
Russell, S. et al., “Artificial Intelligence: A Modern Approach; Second Edition”—(§20.6) pp. 749-752. 2003.
Platt et al., “Large Margin DAGs for Multiclass Classification” Advances in Neural Information Processing Systems 12, pp. 547-553, MIT Press. 2000.
Drucker, H. et al., “Support Vector Machines for Spam Categorization” IEEE. Sep. 1999., pp. 1048-1054.
Weston, J. et al., “Support Vector Machines for Multi-Class Pattern Recognition” Proceedings of the Seventh European Symposium on Artificial Neural Networks. 1999., 6 pages.
Fragoudis, D. et al., “Integrating Feature and Instance Selection for Text Classification” SIGKDD'02. ACM. Jul. 2002, pp. 501-506.
Final Office Action Summary from U.S. Appl. No. 11/752,691 mailed on Sep. 3, 2010.
Final Office Action Summary from U.S. Appl. No. 11/752,719 mailed on May 28, 2010.
Notice of Allowance from U.S. Appl. No. 11/752,719 dated Dec. 28, 2010.

Affiliated with

Also associated with

Rating

Data classification methods using machine learning techniques does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Data classification methods using machine learning techniques, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Data classification methods using machine learning techniques will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-2719339