Method for automatically assigning priorities to documents and messages

• US 20070271504A1
• Filed: 05/21/2003
• Published: 11/22/2007
• Est. Priority Date: 07/30/1999
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Horvitz, Eric
• Granted Patent: US 8,166,392 B2
• Time in Patent Office: Days
• US Class Current: 715/236
• CPC Class Codes: G06Q 10/107 Computer-aided management of electronic mailing [e-mailing]
• CPC Class Codes: Y10S 707/99937 Sorting

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.

Claims

1. A computer-implemented method comprising:

receiving a text;

generating a priority of the text based on a text classifier, and,

outputting the priority.

2. The method of claim 1, wherein receiving a text comprises receiving an email.

3. The method of claim 1, wherein generating a priority comprises generating the priority based on a text classifier comprising a Bayesian classifier.

4. The method of claim 1, wherein generating a priority comprises generating the priority based on a text classifier comprising a support-vector machine classifier.

5. The method of claim 1, further initially comprising training the text classifier.

6. The method of claim 5, wherein training the text classifier comprises seeding the text classifier to discriminate features of a text that are considered important.

7. The method of claim 1, wherein generating a priority comprises generating a priority as a likelihood the text is of high priority.

8. The method of claim 1, wherein outputting the priority comprises alerting the user based on the priority.

9. The method of claim 1, wherein outputting the priority comprises:

determining an expected loss of non-review of the text at a current time;

determining an expected cost of alerting the user of the text at the current time; and

,

alerting the user of the text upon determining that the expected loss is greater than the expected cost.

10. The method of claim 9, wherein determining an expected loss of non-review of the text at a current time considers the expected loss of non-review of the text at a future time when the user would otherwise review the message.

11. The method of claim 9, wherein determining an expected loss comprises initially determining a current expected rate of lost opportunity as a function of present time.

12. The method of claim 9, wherein determining an expected cost comprises determining a probability inference as to a likelihood of the user being present.

13. The method of claim 1, further comprising continually training the text classifier.

14. A computer implemented method comprising:

training a text classifier comprising one of a Bayesian classifier and a support vector machine classifier;

receiving a text;

generating a priority of the text based on the text classifier; and

,

outputting the priority.

15. The method of claim 14, further comprising continually training the text classifier.

16. The method of claim 14, wherein the text classifier comprises seeding the text classifier to discriminate features of a text that are considered important.

17. The method of claim 14, wherein generating a priority comprises generating a priority as a likelihood the text is of high priority.

18. The method of claim 14, wherein outputting the priority comprises:

determining an expected loss of non-review of the text at a current time;

determining an expected cost of alerting the user of the text at the current time; and

,

alerting the user of the text upon determining that the expected loss is greater than the expected cost.

19. The method of claim 18, wherein determining an expected loss of non-review of the text at a current time considers the expected loss of non-review of the text at a future time when the user would otherwise review the message.

20. The method of claim 18, wherein determining an expected loss comprises initially determining a current expected rate of lost opportunity as a function of present time.

21. The method of claim 18, wherein determining an expected cost comprises determining a probability inference as to a likelihood of the user being present.

22. A machine-readable medium having instructions stored thereon for execution by a processor to perform a method comprising:

receiving a text;

generating a priority of the text based on a text classifier; and

,

out putting the priority.

23. The medium of claim 22 wherein receiving a text comprises receiving an email.

24. The medium of claim 22 wherein generating a priority comprises generating the priority based on a text classifier comprising one of a Bayesian classifier and a support-vector machine classifier.

25. The medium of claim 22 further initially comprising training the text classifier by seeding the text classifier to discriminate features of a text that are considered important.

26. The medium of claim 22 wherein outputting the priority comprises:

determining an expected loss of non-review of the text at a current time;

determining an expected cost of alerting the user of the text at the current time; and

,

alerting the user of the text upon determining that the expected loss is greater than the expected cost.

27. The medium of claim 26, wherein determining an expected loss of non-review of the text at a current time considers the expected loss of non-review of the text at a future time when the user would otherwise review the message.

28. The medium of claim 26, wherein determining an expected loss comprises initially determining a current expected rate of lost opportunity as a function of present time.

29. The medium of claim 26, wherein determining an expected cost comprises determining a probability inference as to a likelihood of the user being present.

30. A computerized system comprising:

a program to generate a text; and

,

a text classifier to generate a priority of the text.

31. The system of claim 30, wherein the program comprises an electronic mail program to receive an electronic mail as the text.

32. The system of claim 30, wherein the text classifier comprises a Bayesian text classifier.

33. The system of claim 30, wherein the text classifier comprises a support-vector-machine classifier.

34. The system of claim 30, wherein the priority comprises a likelihood the text is of high priority.

35. The system of claim 30, wherein at least one of the program and the text classifier comprise a computer program executed by a processor from a computer-readable medium.