Determining variation sets among product descriptions

Data processing: database and file management or data structures – Database and file access – Preparing data for information retrieval

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C707S917000

Reexamination Certificate

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07970773

ABSTRACT:
Systems and methods for determining a set of variation-phrases from a collection of documents in a document corpus is presented. Potential variation-phrase pairs among the various documents in the document corpus are identified. The identified potential variation-phrase pairs are then added to a variation-phrase set. The potential variation-phrase pairs in the variation-phrase set are filtered to remove those potential variation-phrase pairs that do not satisfy a predetermined criteria. After filtering the variation-phrase set, the resulting variation-phrase set is stored in a data store.

REFERENCES:
patent: 4849898 (1989-07-01), Adi
patent: 5062074 (1991-10-01), Kleinberger
patent: 5261112 (1993-11-01), Futatsugi
patent: 5835892 (1998-11-01), Kanno
patent: 5960383 (1999-09-01), Fleischer
patent: 6038561 (2000-03-01), Snyder
patent: 6075896 (2000-06-01), Tanaka
patent: 6076086 (2000-06-01), Masuichi
patent: 6167398 (2000-12-01), Wyard
patent: 6173251 (2001-01-01), Ito
patent: 6263121 (2001-07-01), Melen
patent: 6606744 (2003-08-01), Mikurak
patent: 6810376 (2004-10-01), Guan
patent: 6961721 (2005-11-01), Chaudhuri
patent: 7113943 (2006-09-01), Bradford
patent: 7346839 (2008-03-01), Acharya
patent: 7386441 (2008-06-01), Kempe
patent: 7426507 (2008-09-01), Patterson
patent: 7529756 (2009-05-01), Haschart
patent: 7562088 (2009-07-01), Daga
patent: 7567959 (2009-07-01), Patterson
patent: 7599914 (2009-10-01), Patterson
patent: 7603345 (2009-10-01), Patterson
patent: 2002/0016787 (2002-02-01), Kanno
patent: 2003/0065658 (2003-04-01), Matsubayashi
patent: 2003/0101177 (2003-05-01), Matsubayashi
patent: 2004/0059736 (2004-03-01), Willse et al.
patent: 2005/0187916 (2005-08-01), Levin et al.
patent: 2006/0112128 (2006-05-01), Brants
patent: 2006/0282415 (2006-12-01), Shibata
patent: 2007/0076936 (2007-04-01), Li et al.
patent: 2010/0049709 (2010-02-01), Ravikumar et al.
patent: 2010/0169327 (2010-07-01), Lindsay et al.
patent: 1 380 966 (2004-01-01), None
Wikipedia Definition, “Jaccard Index”, Aug. 11, 2010, Wikimedia Foundation Inc.
Wikipedia Definition, “Needleman Wunsch” Jun. 10, 2010, Wikimedia Foundation Inc., pp. 1-5.
Wikipedia Definition, “Sequence Alignment”, Aug. 5, 2010, Wlkimedia Foundation Inc., p. 1.
Ghahramani, Z., and K.A. Heller, “Bayesian Sets,” Advances in Neural Information Processing Systems 18 (2006).
“Google Sets,” © 2007 Google, <http://labs.google.com/sets>, [retrieved Feb. 13, 2008].
Bilenko, M., et al., “Adaptive Name Matching in Information Integration,” IEEE Intelligent Systems 18(5):16-23, Sep./Oct. 2003.
Kilgarriff, A., “Using Word Frequency Lists to Measure Corpus Homogeneity and Similarity between Corpora,” Information Technology Research Institute Technical Report Series, ITRI-97-07, University of Brighton, U.K., Aug. 1997, 16 pages.
Ramos, J., “Using TF-IDF to Determine Word Relevance in Document Queries,” Proceedings of the First Instructional Conference on Machine Learning (iCML-2003), Piscataway, N.J., Dec. 3-8, 2003, 4 pages.

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