Method and apparatus for assembling a set of documents related to a triggering item

• US 9,904,681 B2
• Filed: 07/13/2011
• Issued: 02/27/2018
• Est. Priority Date: 01/12/2009
• Status: Active Grant

• Current Assignee: SRI International, Inc.
• Original Assignee: SRI International, Inc.
• Inventors: Nitz, Kenneth, Dunkley, David, Donneau-Golencer, Thierry, Cheyer, Adam, Pound, Leslie, Hardt, Stephen L.
• Primary Examiner(s): GORTAYO, DANGELINO N
• Application Number:
  US13/182,245
• Publication Number:
  US 20120131020A1
• Time in Patent Office: 2,421 Days
• Field of Search: 707749, 707702, 707687, 707705, 707706, 707709, 707741, 707748, 707755, 707769, 707782, 707802, 707804, 707100, 707104
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/338 Presentation of query results
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06V 30/418 Document matching, e.g. of document images
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

The present invention relates to a method and apparatus for assembling a set of documents related to a triggering item. One embodiment of a method for assembling a set of electronic documents related to an electronic triggering item detected by a computing device being operated by a user includes automatically extracting by the computing device a set of features from the triggering item, without receiving a request by the user to assemble the set of electronic documents, and assembling as the set of electronic documents a plurality of documents that is relevant to the set of features, wherein the plurality of documents is retrieved from a plurality of different types of electronic sources.

Claims

1. A computer-implemented method for assembling a set of electronic documents related to an electronic triggering item, the method comprising:

using a machine-learning based classifier, classifying electronic documents in a set of harvested documents as being associated with at least one activity type, the at least one activity type describing an event involving human activity in the physical world;

when the triggering item is received, identifying an activity type based on the triggering item;

using the machine learning-based classifier, selecting a template that corresponds to the activity type that is based on the triggering item, wherein the selected template is selected from a set of templates that are associated with different activity types and the templates in the set of templates include lists of different types of documents that are associated with the different activity types;

using the selected template, ranking the documents in the set of harvested documents based on how closely the documents in the set of harvested documents match the activity type that is based on the triggering item, wherein the ranking comprises increasing the ranking of a document when an activity type of the document matches the activity type associated with the template and decreasing the ranking of a document when an activity type of the document does not match the activity type associated with the template;

using the ranking, outputting the set of electronic documents relevant to the triggering item.

2. The method of claim 1, wherein the triggering item comprises an electronic reference to at least one user activity, and the set of features relates to the at least one user activity.

3. The method of claim 2, wherein the triggering item comprises an electronic mail message.

4. The method of claim 2, wherein the triggering item comprises an electronic file.

5. The method of claim 2, wherein the triggering item comprises an entry in a calendar application.

6. The method of claim 2, wherein the at least one user activity comprises a trip.

7. The method of claim 2, wherein the at least one user activity comprises a meeting.

8. The method of claim 7, wherein the meeting comprises an employment interview.

9. The method of claim 2, wherein the at least one user activity comprises a proposal activity.

10. The method of claim 2, wherein the at least one user activity comprises a review of a technical paper.

11. The method of claim 1, comprising extracting a set of features from the triggering item, wherein the set of features comprises at least one of:

a keyword, a person, a date, a location, or an acronym.

12. The method of claim 1, comprising extracting a set of features from the triggering item, wherein the set of features defines an activity type associated with the triggering item.

13. The method of claim 1, comprising:

extracting a set of features from the triggering item

building a query in accordance with the set of features;

running a search using the query over a plurality of available documents from the plurality of different types of electronic sources.

14. The method of claim 13, wherein the query comprises a weight associated with at least one feature in the set of features.

15. The method of claim 13, wherein the running produces a list of documents ranked according to relevance to the set of features and document frequency.

16. The method of claim 13, further comprising:

automatically harvesting the plurality of available documents using a classifier.

17. The method of claim 1, wherein the plurality of documents is retrieved from a plurality of different types of electronic sources.

18. The method of claim 17, wherein the plurality of different types of electronic sources includes a local storage device of the computing device.

19. The method of claim 1, further comprising:

re-ranking the set of documents.

20. The method of claim 1, further comprising:

outputting the plurality of documents to an input/output device.

21. The method of claim 20, further comprising:

receiving feedback relating to at least one document in the set of harvested documents.

22. The method of claim 21, further comprising:

adjusting the ranking in accordance with the feedback.

23. The method of claim 22, wherein the adjusting comprises updating a global dictionary used to guide the ranking.

24. The method of claim 22, wherein the adjusting comprises associating the set of harvested documents with a signature of the triggering item.

25. The method of claim 22, wherein the adjusting comprises adjusting an algorithm or model used to assign a score to the set of harvested documents.

26. The method of claim 1, wherein the plurality of documents includes at least one personal document.

27. The method of claim 1, wherein the triggering item is a single item detected by the computing device.

28. The method of claim 1, comprising, when information relevant to the triggering item is not included in the set of electronic documents, generating an alert and outputting the alert with the outputting of the set of electronic documents.

29. A non-transitory computer readable medium containing an executable program for assembling a set of electronic documents related to an electronic triggering item, where the program performs steps comprising:

using a machine-learning based classifier, classifying electronic documents in a set of harvested documents as being associated with at least one activity type, the at least one activity type describing an event involving human activity in the physical world;

when the triggering item is received, identifying an activity type based on the triggering item;

using the machine learning-based classifier, selecting a template that corresponds to the activity type that is based on the triggering item, wherein the selected template is selected from a set of templates that are associated with different activity types and the templates in the set of templates include lists of different types of documents that are associated with the different activity types;

using the selected template, ranking the documents in the set of harvested documents based on how closely the documents in the set of harvested documents match the activity type that is based on the triggering item, wherein the ranking comprises increasing the ranking of a document when an activity type of the document matches the activity type associated with the template and decreasing the ranking of a document when an activity type of the document does not match the activity type associated with the template;

using the ranking, outputting the set of electronic documents relevant to the triggering item.

30. A system for assembling a set of electronic documents related to an electronic triggering item, the system comprising at least one processor for:

classifying electronic documents in a set of harvested documents as being associated with at least one activity type, the at least one activity type describing an event involving human activity in the physical world;

when the triggering item is received, identifying an activity type based on the triggering item;

using the machine learning-based classifier, selecting a template that corresponds to the activity type that is based on the triggering item, wherein the selected template is selected from a set of templates that are associated with different activity types and the templates in the set of templates include lists of different types of documents that are associated with the different activity types;

using the selected template, ranking the documents in the set of harvested documents based on how closely the documents in the set of harvested documents match the activity type that is based on the triggering item, wherein the ranking comprises increasing the ranking of a document when an activity type of the document matches the activity type associated with the template and decreasing the ranking of a document when an activity type of the document does not match the activity type associated with the template;

using the ranking, outputting the set of electronic documents relevant to the triggering item.