System and method for contextual correlation of web document content

• US 20050262428A1
• Filed: 05/21/2004
• Published: 11/24/2005
• Est. Priority Date: 05/21/2004
• Status: Abandoned Application

• Current Assignee: Mygeek Com
• Original Assignee: Mygeek Com
• Inventors: Ballapragada, Bhaskar, Little, Chad M., Ainsa, Michael
• Application Number:
  US10/850,858
• Publication Number:
  US 20050262428A1
• Time in Patent Office: Days
• US Class Current: 715/201
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/972 Access to data in other repository systems, e.g. legacy data or dynamic Web page generation

Abstract

An exemplary system and method for contextually correlating web page document text is disclosed as comprising inter alia: a context engine for analyzing at least a portion of web page document content in order to identify textual components therein as correlation candidates; and a correlation engine for marking-up or otherwise identifying textual components in association with related keywords and/or link destinations. Disclosed features and specifications may be variously controlled, adapted or otherwise optionally modified to improve correlation and/or embedded markup of web page document content for any application or operating environment. Exemplary embodiments of the present invention generally provide enhanced online searching and advertising capabilities.

Claims

1. A system for correlating web page text with at least one of a link destination and dynamic document content, said system comprising:

a context engine suitably adapted to perform contextual analysis of at least a portion of said web page text in order to identify at least one textual component as a candidate for correlation; and

a correlation engine suitably adapted to mark-up said textual component to indicate that said textual component is correlated to at least one of said link destination and said dynamic document content.

2. The system of claim 1, wherein said dynamic document content comprises at least one of a keyword suggestion and an associated link.

3. The system of claim 1, wherein said dynamic document content comprises at least one of a plurality of keyword suggestions and a plurality of associated links.

4. The system of claim 1, wherein said context engine and said correlation engine are embodied in at least one of a substantially unitary hardware product and a substantially unitary software product.

5. The system of claim 1, wherein said markup of said textual component comprises at least one of a graphic decoration of said textual component and an ‘

OnMouseOver’

event.

6. The system of claim 1, further comprising:

at least one database;

said database having at least one of keyword data and web page document data stored therein; and

said database hosted from at least one of a local server, a local network server and a remote server.

7. The system of claim 1, further comprising at least one of a web server, a management interface and a web browser client.

8. The system of claim 7, wherein said management interface comprises at least one of a context engine management interface and a correlation engine management interface.

9. The system of claim 1, wherein said context engine is suitably adapted to perform contextual analysis at least partially based on at least one of a lookup table, word frequency, word density, in-bound IP address, in-bound links, out-bound links, historical user link destinations, browser cache data, URL history data, word relevance values, previously rendered document content, a word weighting metric, a phrase weighting metric, keyword ad pricing, word clustering, a weighted average function, and historical feedback.

10. A method for contextual correlation of web page document content, said method comprising the steps of:

providing at least a portion of content from a web page document;

optionally parsing at least a portion of said document content;

performing a contextual analysis of at least a portion of said optionally parsed document content in order to identify at least one text component of said document content as a candidate for correlation;

correlating said document text component to at least one of a keyword and an associated link destination ; and

adding at least one identifying element to the document content to provide an indication that said text component is correlated to at least one of said keyword and said associated link destination.

11. The method of claim 10, wherein said correlated text component comprises at least one of a word, a clause, a phrase and a sentence.

12. The method of claim 11, further comprising the step of rendering the resulting web page document to an end-user on a web browser.

13. The method of claim 10, further comprising the step of eliminating at least one text component from submission as a candidate for contextual analysis based on at least one of word frequency and contextual ambiguity.

14. The method of claim 10, where said step of contextual analysis is at least partially based on at least one of a lookup table, word frequency, word density, in-bound IP address, in-bound links, out-bound links, historical user link destinations, browser cache data, URL history data, a word relevance value, previously rendered document content, a word weighting metric, a phrase weighting metric, keyword ad pricing, word clustering, a weighted average function, and historical feedback.

15. The method of claim 10, further comprising the step of providing at least one suggested keyword to better characterize the user'"'"'s contextual interest in the document content.

16. The method of claim 15, wherein said suggested keyword is correlated to at least one of a second suggested keyword and a second associated link destination.

17. The iterative application of any of the steps of claim 15 for the optimized correlation of document content in order to provide at least one of a plurality of related keywords and a plurality of associated link destinations.

18. A system for processing web page document content, said system comprising:

a web content publisher;

said publisher providing at least a portion of web document content for correlation analysis;

said publisher optionally configured with a database for at least one of storing and serving said web document content;

a keyword management agent;

an advertiser, said advertiser providing at least one keyword to said agent, said keyword associated with at least one destination link;

said agent suitably configured to access said web document content;

said agent suitably configured for optionally parsing at least a portion of said web document content;

said agent suitably configured for performing contextual analysis of at least a portion of said optionally parsed document content in order to identify at least one text component of said document content as a candidate for correlation;

at least one of said agent and said publisher suitably configured for correlating said document text component to at least one of said keyword and said associated destination link;

at least one of said agent and said publisher suitably configured for adding at least one identifying element to the web document content to provide an indication that said document text component is correlated to at least one of said keyword and said associated destination link; and

said identifying element optionally comprising at least one of an underline, a double underline, a strike-through, a superscript, a subscript, an italicized font, a bold font, capitalization, case, color, shape, a pop-up window, a DHTML layer, status bar text, sound, animation, a video clip, an image, a variant cursor graphic, and an icon.

19. The system of claim 18, where said contextual analysis is at least partially based on at least one of a lookup table, word frequency, word density, in-bound IP address, in-bound links, out-bound links, historical user link destinations, browser cache data, URL history data, a word relevance value, previously rendered document content, a word weighting metric, a phrase weighting metric, keyword ad pricing, word clustering, a weighted average function, and historical feedback.

20. The system of claim 18, wherein said agent further comprises at least one of a context engine management interface and a correlation engine management interface.