SEMANTIC DISCOVERY ENGINE

• US 20070043761A1
• Filed: 08/22/2006
• Published: 02/22/2007
• Est. Priority Date: 08/22/2005
• Status: Abandoned Application

• Current Assignee: The Personal Bee, Inc. (Synacor, Inc.)
• Original Assignee: The Personal Bee, Inc. (Synacor, Inc.)
• Inventors: Chim, Nicholas, Shelton, Edward
• Application Number:
  US11/466,280
• Publication Number:
  US 20070043761A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/3323 using document space presentation or visualization, e.g. category, hierarchy or range presentation and selection
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/958 Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking

Abstract

Discovering topics of interest from the content received from multiple sources. Content from multiple sources is aggregated and stored in a database along with the content'"'"'s metadata. Phrases are extracted from the content and scored based on at least a time window for each phrase. The high ranking phrases are presented to a user via a user interface. When a user selects a particular phrase, content corresponding to the selected phrase is presented to the user. The content may include a list of ranked documents or a specific document. The phrases presented to the user can also be topic specific.

Claims

1. A method for discovering topics of content from one or more sources of the content, the method comprising:

aggregating content from one or more sources;

extracting phrases from the content; and

determining a phrase score for each phrase extracted from the content.

2. The method of claim 1, further comprising providing a phrase cloud to a user, the phrase cloud including one or more phrases that are selected based on at least the phrase scores, wherein the one or more phrases are associated with specific documents included in the content from the one or more sources.

3. The method of claim 1, wherein aggregating content from one or more sources further comprises one or more of:

downloading documents from the one or more sources and storing the documents in a database; and

storing metadata of the documents in the database.

4. The method of claim 1, wherein aggregating content from one or more sources further comprises aggregating content from one or more of RSS feeds, websites, e-mail newsletters, e-mails, newsgroups, videos, multimedia content, or audio transcripts.

5. The method of claim 1, wherein extracting phrases from the content further comprises one or more of:

inferring phrases or portions of phrases for at least one document;

identifying one or more phrases for each document;

or

identifying the phrases using stop words, weak words, prepositions, adverbs, punctuation or dictionaries.

6. The method of claim 1, wherein extracting phrases from the content further comprises associated a time window for each phrase and counting occurrences of each phrase in the content.

7. The method of claim 1, wherein determining a phrase score for each phrase extracted from the content further comprises identifying a time window for each phrase.

8. The method of claim 7, wherein determining a phrase score for each phrase extracted from the content further comprises one or more of:

comparing the time window with a prior time window;

identifying a frequency in the time window for each phrase;

identifying a historical frequency for each phrase;

accounting for a source of each phrase;

receiving editorial discretion from an authorized user;

or

user behavioral data.

9. The method of claim 8, wherein the user behavioral data includes at least one of clicks on a particular phrase, articles viewed, or pages that are accessed by the user.

10. The method of claim 1, further comprising removing duplicate phrases.

11. The method of claim 10 wherein removing duplicate phrases further comprises one or more of;

removing phrases that are completely contained in other phrases;

considering documents returned for each phrase;

or

considering phrase scores for each phrase.

12. The method of claim 1, wherein providing a phrase cloud to a user further comprises displaying the phrase cloud with visual cues, the visual clues enabling a user to select a specific phrase.

13. The method of claim 12, wherein the visual cues include one or more of phrase color and font size, the font size in proportion to a ranking of each phrase in the phrase cloud.

14. The method of claim 1, wherein only phrases with a high ranking are included in the phrase cloud.

15. The method of claim 1, wherein the phrase cloud is generated for a particular topic or edition.

16. The method of claim 1, further comprising presenting a ranked list of documents based on selection of a specific phrase in the phrase cloud by a user.

17. The method of claim 16, further comprising presenting a particular document to the user that is selected from the ranked list of documents.

18. In a system that includes one or more clients having access to a network, a semantic engine for discovering topics of interest from content provided by multiple sources, the semantic engine comprising:

an aggregator that receives content from one or more sources and stores the content and metadata of the content in a database;

a feature extraction module that identifies phrases from the content and metadata stored in the database;

a statistical engine that counts occurrences of each phrase in the content, wherein the occurrences are also associated with one or more time windows; and

a ranking method module that generates phrase scores for the phrases stored in the database, wherein the ranking method module uses the one or more time windows associated with each phrase to generate the phrase scores of the phrases.

19. The semantic engine of claim 18, further comprising a presentation module that generates a phrase cloud for display to an end user, the phrase cloud including a set of phrases having the highest phrase scores.

20. The semantic engine of claim 18, further comprising a module that eliminates duplicate phrases based on one or more of:

determining whether a phrase is subsumed in another phrase;

comparing documents returned by one or more phrases; and

comparing phrase scores.

21. The semantic engine of claim 18, further comprising one or more topical dictionaries, wherein each topical dictionary can determine relevancy of a particular phrase for a particular topic.

22. The semantic engine of claim 18, wherein the ranking method module generates the phrase scores based on one or more of a time window of interest, a start time, a frequency in the time window of interest, a historical frequency, a source of the content, user behavioral data, or editorial discretion.

23. The semantic engine of claim 18, wherein the phrase could is at least one of based on extracted content, dynamically generated, and time relevant.

24. The semantic engine of claim 18, wherein the phrase cloud includes visual cues including font size to indicate ranking and color to distinguish one phrase from the next.

25. The semantic engine of claim 18, wherein the occurrences of a phrase are used for topic categorization.

26. A method for discovering content from one or more sources of the content, the method comprising:

aggregating content from one or more sources at a database, including metadata for the content;

extracting phrases from the content and from the metadata, wherein the extracted phrases are associated with one or more time periods; and

determining a phrase score for each phrase extracted from the content, wherein the phrase score for each phrase has a time dependency.