IP Library › Granted Patent US 8,849,725
Granted Patent B2
US 8,849,725 · App. 12/538,776 · Granted Sep 30, 2014

Automatic classification of segmented portions of web pages

Inventors: Lei Duan (San Jose, CA); Fan Li (Sunnyvale, CA); Srinivas Vadrevu (Milpitas, CA); Emre Velipasaoglu (Sunnyvale, CA); Swapnil Hajela (Fremont, CA); Deepayan Chakrabarti (Mountain View, CA)
Assignee: Yahoo! Inc.
G06F17/30873G06K9/6256G06F15/18G06N99/005G06Q10/10
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Quick Facts
Patent No.
US 8,849,725
App. No.
12/538,776
Granted
Sep 30, 2014
Kind
B2
Abstract

Exemplary methods and apparatuses are provided which may be used for classifying and indexing segmented portions of web pages and providing related information for use in information extraction and/or information retrieval systems.

Claims (50)

1. A method comprising:

with one or more special purpose computing devices:

for at least one of a plurality of segmented portions obtained from at least one of A plurality of displayable web pages as represented by one or more digital signals of one or more data files, using one or more machine learned models to:

identify one or more feature properties of said segmented portion, wherein at least one of said one or more feature properties affects a presentation of said segmented portion within a rendered version of at least one displayable web page and corresponds to one or more query dependent properties based, at least in part, on one or more historical queries for said at least one of a plurality of displayable web pages;

classify said segmented portion as being at least one of a plurality of segment types based, at least in part, on said one or more identified feature properties; and

generating one or more digital signals representative of at least part of an index for said plurality of segmented portions, said index being based, at least in part, on said segment type.

2. The method as recited in claim 1 , further comprising:

with said one or more special purpose computing devices, training at least one of said one or more machine learned models based, at least in part, on editorial input from a sample set of segmented portions.

3. The method as recited in claim 2 , wherein said one or more machine learned models operating in an unsupervised mode identifies one or more digital signals representing a vector space representation as one of said feature properties.

4. The method as recited in claim 1 , wherein at least one of said one or more machine learned models operates in an unsupervised mode.

5. The method a recited in claim 1 , wherein at least one of said plurality of segmented portions comprises one or more digital signals representing at least one document object model (DOM) node.

6. The method as recited in claim 1 , further comprising:

with said one or more special purpose computing devices:

accessing said at least one data file of said at least one of said plurality of displayable web pages; and

identifying one or more digital signals representing said plurality of segmented portions based, at least in part, on an initial set of properties identifiable in one or more digital signals representing said at least one data file.

7. The method as recited in claim 1 , wherein at least one other feature property corresponds to a likelihood of a particular user interaction via said segmented portion within said rendered version, said likelihood of user interaction being based, at least in part, on previously obtained web traffic data.

8. The method as recited in claim 1 , wherein at least one other feature property corresponds to a likelihood of a particular user viewing response to said segmented portion within said rendered version, said likelihood of user viewing response being based, at least in part, on previously obtained user viewing response studies.

9. The method as recited in claim 1 , wherein at least one other feature property corresponds to a distribution of link properties within said rendered version.

10. The method as recited in claim 1 , wherein at least one other feature property corresponds to a distribution of user interface features of said rendered version.

11. The method as recited in claim 1 , wherein said one or more historical queries comprises one or more top N historical queries retrieving said at least one of a plurality of displayable web pages, and said one or more query dependent properties is based, at least in part, on one or more of a position of match, and/or a quality of match.

12. An apparatus comprising:

memory having stored therein one or more digital signals representing at least one data file of at least one displayable web page;

at least one processing unit coupled to said memory and programmed with instructions to:

for at least one of a plurality of segmented portions obtained from said displayable web page, use one or more machine learned models to:

identify one or more feature properties of said segmented portion, wherein at least one of said one or more feature properties affects a presentation of said segmented portion within a rendered version of at least one displayable web page and corresponds to one or more query dependent properties based, at least in part, on one or more historical queries for said at least one of a plurality of displayable web pages;

classify said segmented portion as being at least one of a plurality of segment types based, at least in part, on said one or more identified feature properties; and

establish an index for said plurality of segmented portions that is based, at least in part, on said segment type.

13. The apparatus as recited in claim 12 , wherein at least one of said one or more machine learned models operates in an unsupervised mode and identifies one or more digital signals representing a vector space representation as one of said feature properties.

14. The apparatus as recited in claim 12 , wherein said at least one processing unit is programmed with instructions to identify said plurality of segmented portions based, at least in part, on an initial set of properties identifiable in said at least one data file.

15. The apparatus as recited in claim 12 , wherein at least one other feature property corresponds to:

a likelihood of a particular user interaction via said segmented portion within said rendered version, said likelihood of user interaction being based, at least in part, on previously obtained web traffic data;

a likelihood of a particular user viewing response to said segmented portion within said rendered version, said likelihood of user viewing response being based, at least in part, on previously obtained user viewing response studies;

a distribution of link properties within said rendered version;

a distribution of user interface features of said rendered version; or

some combination thereof.

16. The apparatus as recited in claim 12 , wherein said one or more historical queries comprises one or more top N historical queries retrieving said at least one of a plurality of displayable web pages, and said one or more query dependent properties is based, at least in part, on one or more of a position of match or a quality of match.

17. An article comprising:

a non-transitory computer readable medium having computer implementable instructions stored thereon that are executable by one or more processing units in a computing device to:

for at least one of a plurality of segmented portions obtained from at least one of a plurality of displayable web pages as represented by one or more digital signals of one or more data files, use one or more machine learned models to:

identify one or more feature properties of said segmented portion, wherein at least one of said one or more feature properties affects a presentation of said segmented portion within a rendered version of at least one displayable web page and corresponds to one or more query dependent properties based, at least in part, on one or more historical queries for said at least one of a plurality of displayable web pages;

classify said segmented portion as being at least one of a plurality of segment types based, at least in part, on said one or more identified feature properties; and

maintain an index for said plurality of segmented portions that is based, at least in part, on said segment type.

18. The article as recited in claim 17 , wherein at least one of said one or more machine learned models operates in an unsupervised mode and identifies one or more digital signals representing a vector space representation as one of said feature properties.

19. The article as recited in claim 17 , wherein at least one other feature property corresponds to:

a likelihood of a particular user interaction via said segmented portion within said rendered version, said likelihood of user interaction being based, at least in part, on previously obtained web traffic data;

a likelihood of a particular user viewing response to said segmented portion within said rendered version, said likelihood of user viewing response being based, at least in part, on previously obtained user viewing response studies;

a distribution of link properties within said rendered version;

a distribution of user interface features of said rendered version; or

some combination thereof.

20. The article as recited in claim 17 , wherein said one or more historical queries comprises one or more top N historical queries retrieving said at least one of a plurality of displayable web pages, and said one or more query dependent properties is based, at least in part, on one or more of a position of match or a quality of match.

Assignments (10)
CORRECTIVE ASSIGNMENT TO CORRECT THE THE ASSIGNOR NAME PREVIOUSLY RECORDED AT REEL: 052853 FRAME: 0153. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Mar 29, 2021
From: R2 SOLUTIONS LLC
To: STARBOARD VALUE INTERMEDIATE FUND LP, AS COLLATERAL AGENT
Reel/Frame 056832/0001 →
CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNEE NAME PREVIOUSLY RECORDED ON REEL 053654 FRAME 0254. ASSIGNOR(S) HEREBY CONFIRMS THE RELEASE OF SECURITY INTEREST GRANTED PURSUANT TO THE PATENT SECURITY AGREEMENT PREVIOUSLY RECORDED. Recorded Dec 30, 2020
From: STARBOARD VALUE INTERMEDIATE FUND LP
To: R2 SOLUTIONS LLC
Reel/Frame 054981/0377 →
RELEASE OF SECURITY INTEREST IN PATENTS Recorded Jul 8, 2020
From: STARBOARD VALUE INTERMEDIATE FUND LP
To: ACACIA RESEARCH GROUP LLC; AMERICAN VEHICULAR SCIENCES LLC; BONUTTI SKELETAL INNOVATIONS LLC; CELLULAR COMMUNICATIONS EQUIPMENT LLC; INNOVATIVE DISPLAY TECHNOLOGIES LLC; LIFEPORT SCIENCES LLC; LIMESTONE MEMORY SYSTEMS LLC; MOBILE ENHANCEMENT SOLUTIONS LLC; MONARCH NETWORKING SOLUTIONS LLC; NEXUS DISPLAY TECHNOLOGIES LLC; PARTHENON UNIFIED MEMORY ARCHITECTURE LLC; R2 SOLUTIONS LLC; SAINT LAWRENCE COMMUNICATIONS LLC; STINGRAY IP SOLUTIONS LLC; SUPER INTERCONNECT TECHNOLOGIES LLC; TELECONFERENCE SYSTEMS LLC; UNIFICATION TECHNOLOGIES LLC
Reel/Frame 053654/0254 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 25, 2020
From: EXCALIBUR IP, LLC
To: R2 SOLUTIONS LLC
Reel/Frame 053459/0059 →
PATENT SECURITY AGREEMENT Recorded Jun 5, 2020
From: ACACIA RESEARCH GROUP LLC; AMERICAN VEHICULAR SCIENCES LLC; BONUTTI SKELETAL INNOVATIONS LLC; CELLULAR COMMUNICATIONS EQUIPMENT LLC; INNOVATIVE DISPLAY TECHNOLOGIES LLC; LIFEPORT SCIENCES LLC; LIMESTONE MEMORY SYSTEMS LLC; MERTON ACQUISITION HOLDCO LLC; MOBILE ENHANCEMENT SOLUTIONS LLC; MONARCH NETWORKING SOLUTIONS LLC; NEXUS DISPLAY TECHNOLOGIES LLC; PARTHENON UNIFIED MEMORY ARCHITECTURE LLC; R2 SOLUTIONS LLC; SAINT LAWRENCE COMMUNICATIONS LLC; STINGRAY IP SOLUTIONS LLC; SUPER INTERCONNECT TECHNOLOGIES LLC; TELECONFERENCE SYSTEMS LLC; UNIFICATION TECHNOLOGIES LLC
To: STARBOARD VALUE INTERMEDIATE FUND LP, AS COLLATERAL AGENT
Reel/Frame 052853/0153 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 3, 2016
From: YAHOO! INC.
To: EXCALIBUR IP, LLC
Reel/Frame 038950/0592 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 1, 2016
From: EXCALIBUR IP, LLC
To: YAHOO! INC.
Reel/Frame 038951/0295 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 18, 2016
From: YAHOO! INC.
To: EXCALIBUR IP, LLC
Reel/Frame 038383/0466 →
CORRECTIVE ASSIGNMENT TO CORRECT THE ADDITION OF THE SIXTH INVENTOR, DEEPAYAN CHAKRABARTI, WHO HAD SIGNED THE ASSIGNMENT BUT WAS NOT LISTED ON THE COVER SHEET PREVIOUSLY RECORDED ON REEL 023086 FRAME 0468. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT RECORDED AUGUST 10, 2009 FROM LEI DUAN ET AL. TO YAHOO! INC.. Recorded Aug 20, 2009
From: DUAN, LEI; LI, FAN; VADREVU, SRINIVAS; VELIPASAOGLU, EMRE; HAJELA, SWAPNIL; CHAKRABARTI, DEEPAYAN
To: YAHOO! INC.
Reel/Frame 023123/0765 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 10, 2009
From: DUAN, LEI; LI, FAN; VADREVU, SRINIVAS; VELIPASAOGLU, EMRE; HAJELA, SWAPNIL
To: YAHOO! INC.
Reel/Frame 023086/0468 →
Continuity (1)
Related Publication 20110035345A1 · Feb 10, 2011