Ranking documents based on large data sets

Data processing: database and file management or data structures – Database design – Data structure types

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C707S793000, C707S793000

Reexamination Certificate

active

10706991

ABSTRACT:
A system ranks documents based, at least in part, on a ranking model. The ranking model may be generated to predict the likelihood that a document will be selected. The system may receive a search query and identify documents relating to the search query. The system may then rank the documents based, at least in part, on the ranking model and form search results for the search query from the ranked documents.

REFERENCES:
patent: 6006222 (1999-12-01), Culliss
patent: 6014665 (2000-01-01), Culliss
patent: 6078916 (2000-06-01), Culliss
patent: 6088692 (2000-07-01), Driscoll
patent: 6182068 (2001-01-01), Culliss
patent: 6285999 (2001-09-01), Page
patent: 6397211 (2002-05-01), Cooper
patent: 6463430 (2002-10-01), Brady et al.
patent: 6539377 (2003-03-01), Culliss
patent: 6546388 (2003-04-01), Edlund et al.
patent: 6546389 (2003-04-01), Agrawal et al.
patent: 6714929 (2004-03-01), Micaelian et al.
patent: 6738764 (2004-05-01), Mao et al.
patent: 6782390 (2004-08-01), Lee et al.
patent: 6799176 (2004-09-01), Page
patent: 7058628 (2006-06-01), Page
patent: 2003/0195877 (2003-10-01), Ford et al.
patent: 2003/0197837 (2003-10-01), Gyu Lee
patent: 2005/0071741 (2005-03-01), Acharya et al.
Justin Boyan et al.; “A Machine Learning Architecture for Optimizing Web Search Engines”; Carnegie Mellon University; May 10, 1996; pp. 1-8.
“Click Popularity—DirectHit Technology Overview”; http://www.searchengines.com/directhit.html; Nov. 10, 2003 (print date); 2 pages.
Co-pending U.S. Appl. No. 10/712,263; Jeremy Bem et al.; “Targeting Advertisements Based on Predicted Relevance of the Advertisements”; filed Nov. 14, 2003, 40 pages.
Co-pending U.S. Appl. No. 10/734,584; Jeremy Bem et al.; “Large Scale Machine Learning Systems and Methods”; filed Dec. 15, 2003, 35 pages.
J.H. Friedman, T. Hastie, and R. Tibshirani; “Additive Logistic Regression: a Statistical View of Boosting”; Dept. of Statistics, Stanford University Technical Report; Aug. 20, 1998.
A.Y. Ng and M.I. Jordan; “On Discriminative vs. Generative classifiers: A comparison of logistic regression and naïve Bayes,” in T. Dietterich, S. Becker and Z. Ghahramani (eds.), Advances in Neural Information Processing Systems 14, Cambridge, MA: MIT Press, 2002.
F. Crestani, M. Lalmas, C. Van Rijsbergen and I. Campbell; ““Is This Document Relevant? . . . Probably”: A Survey of Probabilistic Models in Information Retrieval”; ACM Computing Surveys, vol. 30, No. 4, Dec. 1998.
Jeffrey A. Dean et al., “Ranking Documents Based on User Behavior and/or Feature Data”; U.S. Appl. No. 10/869,057, filed Jun. 17, 2004; 36 pages.

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Ranking documents based on large data sets does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Ranking documents based on large data sets, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Ranking documents based on large data sets will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-3861496

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.