RELATED NOTES AND MULTI-LAYER SEARCH IN PERSONAL AND SHARED CONTENT

• US 20130318063A1
• Filed: 03/28/2013
• Published: 11/28/2013
• Est. Priority Date: 05/24/2012
• Status: Active Grant

• Current Assignee: Bending Spoons SpA
• Original Assignee: Evernote Corp. (Bending Spoons SpA)
• Inventors: Bignert, Jakob, Libin, Phil, Constantinou, Phil, Ayzenshtat, Mark, Chan, Connie
• Granted Patent: US 11,157,570 B2
• Time in Patent Office: Days
• US Class Current: 707/707
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9538 Presentation of query results

Abstract

Displaying relevant data subsets of at least one data collection includes providing an input data subset, determining a plurality of partial relevance values for different characteristics of each of the plurality of data subsets of the at least one data collection with respect to the input data subset according to a term frequency of terms in the input data subset and term in each of the plurality of data subsets of the at least one data collection and according to an inverse document frequency of the terms, determining a relevance of each of the plurality of data subsets of the at least one data collection based on the partial relevance values of the different characteristics, and displaying at least a portion of the data subsets of the at least one data collection in order of relevance.

Claims

1. A method of displaying relevant data subsets of at least one data collection, comprising:

providing an input data subset;

determining a plurality of partial relevance values for different characteristics of each of the plurality of data subsets of the at least one data collection with respect to the input data subset according to a term frequency of terms in the input data subset and terms in each of the plurality of data subsets of the at least one data collection and according to an inverse document frequency of the terms;

determining a relevance of each of the plurality of data subsets of the at least one data collection based on the partial relevance values of the different characteristics; and

displaying at least a portion of the data subsets of the at least one data collection in order of relevance.

2. A method, according to claim 1, wherein the input data subset is one of:

a web page, a document, a portion of a document, a user search query submitted to a public search engine, a user search query submitted to a database, and a pre-processed search result obtained from a public search engine.

3. A method, according to claim 2, wherein the data subsets of the at least one data collection are initially determined using search results from a public search engine to form an extended query that is used to initially search the at least one data collection.

4. A method, according to claim 3, wherein the search results from the public search engine are stripped of generic terms and web links prior to being used to form the extended query.

5. A method, according to claim 4, wherein generic terms are provided by a specially compiled system vocabulary.

6. A method, according to claim 1, wherein the data subsets of the at least one data collection are digital notes and wherein the at least one data collection is a digital notebook.

7. A method, according to claim 6, wherein the digital notes are displayed in a popup pane.

8. A method, according to claim 7, wherein when the input is a web page, the digital notes that are displayed include a title of the web page and snippets of the digital notes that are displayed.

9. A method, according to claim 6, wherein when the input is a user search query submitted to a public search engine, the digital notes that are displayed are shown proximate to search results from the public search engine.

10. A method, according to claim 6, wherein the characteristics include at least one of:

a title, a body portion of the notes, and a tag.

11. A method, according to claim 1, wherein relevance is a weighted sum of the partial relevance values using criteria weights.

12. A method, according to claim 1, wherein relevance is a minimum of the partial relevance values multiplied by criteria weights.

13. A method, according to claim 1, wherein partial relevance of each of the data subsets with respect to each of the characteristics is determined using a cosine similarity between a vector corresponding to term frequencies and inverse document frequencies of each of the characteristics of the input data subset and a vector corresponding to term frequencies and inverse document frequencies of each of the characteristics of each of the data subsets.

14. A method, according to claim 1, wherein the data subsets of the at least one data collection are included in at least one of:

a private data collection and a shared data collection.

15. Computer software, provided in a non-transitory computer-readable medium, that determines relevant data subsets of at least one data collection, the software comprising:

executable code that receives an input data subset;

executable code that determines a plurality of partial relevance values for different characteristics of each of the plurality of data subsets of the at least one data collection with respect to the input data subset according to a term frequency of terms in the input data subset and terms in each of the plurality of data subsets of the at least one data collection and according to an inverse document frequency of the terms; and

executable code that determines a relevance of each of the plurality of data subsets of the at least one data collection based on the partial relevance values of the different characteristics.

16. Computer software, according to claim 15, wherein the input data subset is one of:

a web page, a document, a portion of a document, a user search query submitted to a public search engine, a user search query submitted to a database, and a pre-processed search result obtained from a public search engine.

17. Computer software, according to claim 16, wherein the data subsets of the at least one data collection are initially determined using search results from a public search engine to form an extended query that is used to initially search the at least one data collection.

18. Computer software, according to claim 17, wherein the search results from the public search engine are stripped of generic terms and web links prior to being used to form the extended query.

19. Computer software, according to claim 18, wherein generic terms are provided by a specially compiled system vocabulary.

20. Computer software, according to claim 15, wherein the data subsets of the at least one data collection are digital notes and wherein the at least one data collection is a digital notebook.

21. Computer software, according to claim 20, wherein the digital notes are displayed in a popup pane.

22. Computer software, according to claim 21, wherein when the input is a web page, at least some of the digital notes are displayed and include a title of the web page and snippets of the digital notes that are displayed.

23. Computer software, according to claim 20, wherein when the input is a user search query submitted to a public search engine and the digital notes are displayed proximate to search results from the public search engine.

24. Computer software, according to claim 20, wherein the characteristics include at least one of:

a title, a body portion of the notes, and a tag.

25. Computer software, according to claim 15, wherein relevance is a weighted sum of the partial relevance values using criteria weights.

26. Computer software, according to claim 15, wherein relevance is a minimum of the partial relevance values multiplied by criteria weights.

27. Computer software, according to claim 15, wherein partial relevance of each of the data subsets with respect to each of the characteristics is determined using a cosine similarity between a vector corresponding to term frequencies and inverse document frequencies of each of the characteristics of the input data subset and a vector corresponding to term frequencies and inverse document frequencies of each of the characteristics of each of the data subsets.

28. Computer software, according to claim 15, wherein the data subsets of the at least one data collection are included in at least one of:

a private data collection and a shared data collection.