Method for automatically assigning priorities to documents...

Data processing: presentation processing of document – operator i – Presentation processing of document – Layout

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C707S793000, C709S207000, C709S240000

Reexamination Certificate

active

09364527

ABSTRACT:
Methods for prioritizing documents, such as email messages, is disclosed. In one embodiment, a computer-implemented method first receives a document. The method assigns a measure of priority to the document, by employing a text classifier such as a Bayesian classifier or a support-vector machine classifier. The method then outputs the priority. In one embodiment, the method includes alerting the user about a document, such as an email message, based on the expected loss associated with delays expected in reviewing the document as compared to the expected cost of distraction and transmission incurred with alerting the user about the document.

REFERENCES:
patent: 5377354 (1994-12-01), Scannell et al.
patent: 5493692 (1996-02-01), Theimer et al.
patent: 5694616 (1997-12-01), Johnson et al.
patent: 5864848 (1999-01-01), Horvitz
patent: 5974465 (1999-10-01), Wong
patent: 6021403 (2000-02-01), Horvitz
patent: 6182059 (2001-01-01), Angotti et al.
patent: 6185603 (2001-02-01), Henderson et al.
patent: 6282565 (2001-08-01), Shaw et al.
patent: 6327581 (2001-12-01), Platt
patent: 6424995 (2002-07-01), Shuman
patent: 6442589 (2002-08-01), Takahashi et al.
patent: WO 98/00787 (1998-01-01), None
Cohen, “Learning Rules that Classify E-Mail”, 1996 (as disclosed at http://www-2.cs.cmu.edu/˜wcohen/pubs-t.html).
Lewis, “Evaluating and Optimizing Autonomous Text Classification Systems”, 1995 ACM.
Lewis, David, “Training Algorithms for Linear Text Classifiers”, AT&T Laboratories, 1996.
Apte, Chidanand, Fred Damerau, and Sholom M. Weiss, “Automated Learning of Decision Rules for Text Categorization”, 1994 ACM.
International Search Report dated Aug. 29, 2003, for International Application Ser. No. PCT/US00/20685.
Robert M. Losee Jr., “Minimizing information overload: the ranking of electronic messages”, Received Jul. 18, 1988, Revised Nov. 8, 1988, pp. 179-189.
Thorsten Joachims, “Text Categorized with Support Vector Machines: Learning with Many Relevent Features”, 1998, 6 pages.
J. Breese, D. Heckerman, & C. Kadie (1998). Empirical Analysis of Predictive Algoirthms for Collaborative Filtering. In Proceedings of the Fourteenth Conference on Uncertinaty in Artificial Intelligence, pp. 43-52. AUAI, Morgan Kaufmann, San Francisco.
M. Czerwinski, S. Dumais, G. Robertson, et al. (1999). Visualizing implicit queries for information management and retrieval. In Proceedings of CHI '99, ACM SIGCHI Conference on Human Factors in Computing Systems, Pittsburgh, PA, pp. 560-567. Association for Computing Machinery.
S. Dumais, J. Platt, D. Heckerman, M. Sahami (1998). Inductive learning algorithms and representations for text categorization. In Proceedings of the Seventh International Conference on Information and Knowledge Management, pp. 148-155. Association for Computing Machinery, ACM Press, New York.
E. Horvitz (1999). Principles of mixed-initiative user interfaces. In Proceedings of CHI '99, ACM SIGCHI Conference on Human Factors in Computing Systems, Pittsburgh, PA, pp. 159-166. Association for Computing Machinery.
E. Horvitz, M. Barry (1995). Display of information for time-critical decision making. In Proceedings of the Eleventh Conference on Uncertinaty in Artificial Intelligence, pp. 296-305 Montreal, Canada. Morgan Kaufmann, San Francisco.
E. Horvitz, J. Breese, D. Heckerman, D. Hovel, K. Rommelse (1998). The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 256-265. Morgan Kaufman, San Francisco.
E. Horvitz and G. Rutledge (1991). Time-dependent utility and action under uncertainty. In Proceedings of Seventh Conference on Uncertainty in Artificial Intelligence, Los Angeles, CA, pp. 151-158. Morgan Kaufmann, San Francisco.
E. Horvitz and A. Seiver (1997). Time-critical action: Representations and application. In Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI-97), pp. 250-257 Providence, RI. Morgan Kaufmann, San Francisco.
D. Koller, M. Sahami (1996). Toward optimal feature selection. In Proceedings of Thirteenth Conference on Machine Learning, pp. 284-292, Morgan Kaufmann, San Francisco.
H. Leiberman (1995). An agent that assist web browsing. In Proceedings of IJCAI-95, Montreal Canada. Morgan Kaufmann, San Francisco.
J. Platt (1999). Fast training of support vector machines using sequential minimal optimzation. In Advances in Kernal Methods: Support Vector Learning. MIT Press, Cambridge, MA.
J. Platt (1999). Proobabilistic outputs for support vector machines and comparison to regularized likelihood methods. In Advances in Large Margin Classifiers, MIT Press, Cambridge, MA.
M. Sahami, S. Dumais, D. Heckerman, E. Horvitz (1998). A Bayesian approach to filtering junk e-mail. In Workshop on Learning for Text Categorization, AAAI Technical Report WS-98-05. American Association for Artificial Intelligence, AAAI.
U.S. patent application Ser. No. 09/007,894, Horvitz, filed Jan. 15, 1998.
U.S. patent application Ser. No. 09/055,477, filed Apr. 6, 1998.

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

Method for automatically assigning priorities to documents... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Method for automatically assigning priorities to documents..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method for automatically assigning priorities to documents... will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-3800875

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