Generating and ranking incremental search suggestions for personal content

Patent metadata

Field	Value
Publication number	US-9075898-B1
Application number	US-201313924905-A
Country	US
Kind code	B1
Filing date	Jun 24, 2013
Priority date	Aug 10, 2012
Publication date	Jul 7, 2015
Grant date	Jul 7, 2015

Abstract

Providing incremental search suggestions from a content database include accessing the content database to determine possible candidates for the search suggestions, scoring each of the candidates based at least partially on a non-monotonic document frequency function, where candidates that appear a first amount corresponding to a relatively frequent occurrence in the content database and candidates that appear a second amount corresponding to a relatively infrequent occurrence in the content database both score lower than candidates that appear in the content database with a frequency that is between the first amount and the second amount, and ordering the possible candidates based on at least the scoring. Possible candidates may include named entities and n-grams. The n-grams may only be unigrams and bigrams. Stop words may be filtered out of the n-grams. Scoring may include taking into account term frequency.

First claim

What is claimed is:

1. A method of providing incremental search suggestions from a content database, comprising:
accessing the content database to determine possible candidates for the search suggestions;
scoring each of the candidates based at least partially on a non-monotonic document frequency function, wherein candidates that appear a first amount corresponding to a relatively frequent occurrence in the content database and candidates that appear a second amount corresponding to a relatively infrequent occurrence in the content database both score lower than candidates that appear in the content database with a frequency that is between the first amount and the second amount; and
ordering the possible candidates based on at least the scoring.

2. A method, according to claim 1 , wherein possible candidates include named entities and n-grams.

3. A method, according to claim 2 , wherein the n-grams are only unigrams and bigrams.

4. A method, according to claim 2 , wherein stop words are filtered out of the n-grams.

5. A method, according to claim 1 , wherein scoring includes taking into account term frequency.

6. A method, according to claim 5 , wherein a boost factor is applied to change scores associated with named entities.

7. A method, according to claim 5 , wherein a boost factor is applied to change scores depending upon which of a number of possible parts of a document contains a corresponding candidate.

8. A method, according to claim 7 , wherein the parts of a document include a heading, a tag, a body, a footnote, an endnote, a comment, and an attachment.

9. A method, according to claim 1 , wherein the content database is private or semi-private corporate or personal content database.

10. A method, according to claim 9 , wherein the content database is provided by one of: the Evernote content management software and service and the OneNote® note-taking software product.

11. A method, according to claim 1 , wherein the incremental search suggestions include partially typed search terms.

12. A method, according to claim 11 , wherein the partially typed search terms are at least one of: prefixes and arbitrary contiguous fragments that correspond to possible candidates.

13. A method, according to claim 1 , wherein the incremental search suggestions are expanded to include alternative terms driven by various types of semantic relevance.

14. A non-transitory computer-readable medium containing software that provides incremental search suggestions from a content database, the software comprising:
executable code that accesses the content database to determine possible candidates for the search suggestions;
executable code that scores each of the candidates based at least partially on a non-monotonic document frequency function, wherein candidates that appear a first amount corresponding to a relatively frequent occurrence in the content database and candidates that appear a second amount corresponding to a relatively infrequent occurrence in the content database both score lower than candidates that appear in the content database with a frequency that is between the first amount and the second amount; and
executable code that orders the possible candidates based on at least the scoring.

15. A non-transitory computer-readable medium, according to claim 14 , wherein possible candidates include named entities and n-grams.

16. A non-transitory computer-readable medium, according to claim 15 , wherein the n-grams are only unigrams and bigrams.

17. A non-transitory computer-readable medium, according to claim 15 , wherein stop words are filtered out of the n-grams.

18. A non-transitory computer-readable medium, according to claim 14 , wherein scoring includes taking into account term frequency.

19. A non-transitory computer-readable medium, according to claim 18 , wherein a boost factor is applied to change scores associated with named entities.

20. A non-transitory computer-readable medium, according to claim 18 , wherein a boost factor is applied to change scores depending upon which of a number of possible parts of a document contains a corresponding candidate.

21. A non-transitory computer-readable medium, according to claim 20 , wherein the parts of a document include a heading, a tag, a body, a footnote, an endnote, a comment, and an attachment.

22. A non-transitory computer-readable medium, according to claim 14 , wherein the content database is private or semi-private corporate or personal content database.

23. A non-transitory computer-readable medium, according to claim 22 , wherein the content database is provided by one of: the Evernote content management software and service and the OneNote® note-taking software product.

24. A non-transitory computer-readable medium, according to claim 14 , wherein the incremental search suggestions include partially typed search terms.

25. A non-transitory computer-readable medium, according to claim 24 , wherein the partially typed search terms are at least one of: prefixes and arbitrary contiguous fragments that correspond to possible candidates.

26. A non-transitory computer-readable medium, according to claim 14 , wherein the incremental search suggestions are expanded to include alternative terms driven by various types of semantic relevance.

Assignees

• Evernote Corp

Inventors

• Ayzenshtat Mark
• Currimbhoy Zeesha

Classifications

• G06F17/3097 Primary

  Physics · mapped topic

• G06F16/3322 Primary

  using system suggestions (G06F16/3325 takes precedence) · CPC title

• G06F16/90324 Primary

  using system suggestions · CPC title