IP Library Granted Patent US 11,188,746
Granted Patent B1
US 11,188,746 · App. 16/829,440 · Granted Nov 30, 2021

Systems and methods for deep learning based approach for content extraction

Inventors: Umang Patel (Sunnyvale, CA); Sridharan Palaniappan (Fremont, CA); Rofaida Abdelaal (Sunnyvale, CA); Chun-Han Yao (Merced, CA)
Assignee: Verizon Media Inc.
G06K9/00463G06F40/40G06N3/08G06Q30/0222G06T7/11G06T2207/20084G06T2207/20132
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Quick Facts
Patent No.
US 11,188,746
App. No.
16/829,440
Granted
Nov 30, 2021
Kind
B1
Abstract

Disclosed are systems and methods for extracting content based on image analysis. A method may include receiving content including at least an image depicting a coupon; converting the received content into a larger image including the image depicting the coupon; determining, utilizing one or more neural networks, the image depicting the coupon within the larger image, wherein determining the image depicting the coupon comprises: segmenting a foreground bounding box including the image depicting the coupon from background image portions of the image; cropping the larger image based on the bounding box, wherein the cropped image consists of the image depicting the coupon; determining text included in the cropped image; and extracting information included in the coupon based on the determined text.

Claims (57)

1. A computer-implemented method comprising:

receiving content including at least an image depicting a coupon;

converting the received content into a larger image including the image depicting the coupon;

determining, utilizing one or more neural networks, the image depicting the coupon within the larger image, wherein determining the image depicting the coupon comprises: segmenting a foreground bounding box including the image depicting the coupon from background image portions of the image;

cropping the larger image based on the segmented bounding box, wherein the cropped image consists of the image depicting the coupon;

determining text included in the cropped image; and

extracting information included in the coupon based on the determined text.

2. The computer-implemented method of claim 1 , wherein the coupon comprises text and/or one or more images regarding a product and/or service.

3. The computer-implemented method of claim 1 , wherein determining text included in the cropped image comprises:

utilizing a detection engine to determine text included in the cropped image; and

utilizing a classification engine to classify the determined text.

4. The computer-implemented method of claim 1 , wherein extracting information included in the coupon based on the determined text comprises:

detecting one or more predetermined words among the determined text; and

assigning a tag for each detected predetermined word, wherein the tag indicates a classification for a word.

5. The computer-implemented method of claim 4 , wherein extracting information included in the coupon based on the determined text further comprises:

concatenating one or more detected predetermined words based on the assigned tags for each of the one or more detected predetermined words.

6. The computer-implemented method of claim 1 , further comprising

displaying the extracted information included in the coupon.

7. The computer-implemented method of claim 1 , wherein the content is received in a HyperText Markup Language (HTML) format.

8. A computer system comprising:

at least one memory having processor-readable instructions stored therein; and

at least one processor configured to access the memory and execute the processor-readable instructions to perform a method including:

receiving content including at least an image depicting a coupon;

converting the received content into a larger image including the image depicting the coupon;

determining, utilizing one or more neural networks, the image depicting the coupon within the larger image, wherein determining the image depicting the coupon comprises: segmenting a foreground bounding box including the image depicting the coupon from background image portions of the image;

cropping the larger image based on the segmented bounding box, wherein the cropped image consists of the image depicting the coupon;

determining text included in the cropped image; and

extracting information included in the coupon based on the determined text.

9. The computer system of claim 8 , wherein the coupon comprises text and/or one or more images regarding a product and/or service.

10. The computer system of claim 8 , wherein determining text included in the cropped image comprises:

utilizing a detection engine to determine text included in the cropped image; and

utilizing a classification engine to classify the determined text.

11. The computer system of claim 8 , wherein extracting information included in the coupon based on the determined text comprises:

detecting one or more predetermined words among the determined text; and

assigning a tag for each detected predetermined word, wherein the tag indicates a classification for a word.

12. The computer system of claim 11 , wherein extracting information included in the coupon based on the determined text further comprises:

concatenating one or more detected predetermined words based on the assigned tags for each of the one or more detected predetermined words.

13. The computer system of claim 8 , further comprising

displaying the extracted information included in the coupon.

14. The computer system of claim 8 , wherein the content is received in a HyperText Markup Language (HTML) format.

15. A non-transitory computer-readable medium containing instructions that, when executed by a processor, cause the processor to perform a method comprising:

receiving content including at least an image depicting a coupon;

converting the received content into a larger image including the image depicting the coupon;

determining, utilizing one or more neural networks, the image depicting the coupon within the larger image, wherein determining the image depicting the coupon comprises: segmenting a foreground bounding box including the image depicting the coupon from background image portions of the image;

cropping the larger image based on the segmented bounding box, wherein the cropped image consists of the image depicting the coupon;

determining text included in the cropped image; and

extracting information included in the coupon based on the determined text.

16. The non-transitory computer-readable medium of claim 15 , wherein the coupon comprises text and/or one or more images regarding a product and/or service.

17. The non-transitory computer-readable medium of claim 15 , wherein determining text included in the cropped image comprises:

utilizing a detection engine to determine text included in the cropped image; and

utilizing a classification engine to classify the determined text.

18. The non-transitory computer-readable medium of claim 15 , wherein extracting information included in the coupon based on the determined text comprises:

detecting one or more predetermined words among the determined text;

assigning a tag for each detected predetermined word, wherein the tag indicates a classification for a word; and

concatenating one or more detected predetermined words based on the assigned tags for each of the one or more detected predetermined words.

19. The non-transitory computer-readable medium of claim 15 , further comprising displaying the extracted information included in the coupon.

20. The non-transitory computer-readable medium of claim 15 , wherein the content is received in a HyperText Markup Language (HTML) format.

Assignments (4)
PATENT SECURITY AGREEMENT (FIRST LIEN) Recorded Sep 29, 2022
From: YAHOO ASSETS LLC
To: ROYAL BANK OF CANADA, AS COLLATERAL AGENT
Reel/Frame 061571/0773 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 16, 2021
From: YAHOO AD TECH LLC (FORMERLY VERIZON MEDIA INC.)
To: YAHOO ASSETS LLC
Reel/Frame 058982/0282 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 26, 2020
From: OATH INC.
To: VERIZON MEDIA INC.
Reel/Frame 054258/0635 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 25, 2020
From: PATEL, UMANG; PALANIAPPAN, SRIDHARAN; ABDELAAL, ROFAIDA; YAO, CHUN-HAN
To: OATH INC.
Reel/Frame 052225/0224 →
Cited By (9)
US 12,198,416 US 12,333,840 US 12,367,655 US 12,394,234 US 12,430,932 US 12,444,221 US 12,450,928 US 12,456,300 US 12,511,488