SYSTEM AND METHOD OF IMAGE-BASED PRODUCT IDENTIFICATION

• US 20180197223A1
• Filed: 05/25/2017
• Published: 07/12/2018
• Est. Priority Date: 01/06/2017
• Status: Abandoned Application

• Current Assignee: Dragon-Click Corp.
• Original Assignee: Dragon-Click Corp.
• Inventors: Grossman, Isaac
• Application Number:
  US15/605,609
• Publication Number:
  US 20180197223A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/5866 using information manually generated, e.g. tags, keywords, comments, manually generated location and time information
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06Q 10/1095 Meeting or appointment
• CPC Class Codes: G06Q 30/0621 Item configuration or customization
• CPC Class Codes: G06Q 30/0625 Directed, with specific intent or strategy
• CPC Class Codes: G06V 10/75 Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
• CPC Class Codes: G06V 20/10 Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: G06V 30/10 Character recognition
• CPC Class Codes: G06V 30/142 using hand-held instruments; Constructional details of the instruments
• CPC Class Codes: G06V 30/147 Determination of region of interest
• CPC Class Codes: G06V 30/18038 Biologically-inspired filters, e.g. difference of Gaussians [DoG], Gabor filters
• CPC Class Codes: G06V 30/18086 by performing operations within image blocks or by using histograms

Abstract

Methods and systems are described for automatically identifying and presenting to a user products or services related to an electronic image identified by the user. The products or services may further be related to a user selection of an object within the image. The image may be obtained from a web site accessed by the user, or the image may be captured by the user via a camera of a mobile device. The image or mobile device may be associated with a geographic location, which may be used to further improve product or service search accuracy. Labels associated with aspects of the image may be generated and used to further identify keywords for searching products or services. Genres may be determined from the keywords to identify vendors or order options to search. The search results may be presented to the user as options related to relevant products or services.

Claims

1. A computer-implemented method, comprising:

obtaining, by one or more processors, an electronic image including a representation of an object within the image;

identifying, by one or more processors, a plurality of labels associated with the image, wherein at least one of the labels is associated with the object;

identifying, by one or more processors, a plurality of keywords associated with the object based upon the plurality of labels;

generating, by one or more processors, an option dataset containing a plurality of data entries indicating options based upon the plurality of keywords, each option being associated with acquisition of a product; and

causing, by one or more processors, one or more of the options indicated by the data entries to be presented to a user for review.

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

receiving, at one or more processors, an indication from the user of a location within the electronic image associated with the object,

wherein the plurality of labels are identified based upon a portion of the electronic image associated with the location.

3. The computer-implemented method of claim 2, wherein:

the indication of the location within the electronic image includes a selection of the portion of the image as an area identified by the user; and

identifying the plurality of labels based upon the portion of the electronic image includes analyzing only the portion of the image, without evaluating other portions of the image.

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

identifying, by one or more processors, one or more vendor sites based upon the identified keywords; and

collecting, by one or more processors, option data from the one or more vendor sites based upon the identified keywords,

wherein each data entry in the option dataset includes the option data collected from a corresponding vendor site of the one or more vendor sites.

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

searching, by one or more processors, one or more vendor sites for occurrences of any of the keywords;

identifying, by one or more processors, a number of potential options associated with the occurrences of the keywords;

determining, by one or more processors, the number is below a minimum search results threshold or is above a maximum search results threshold; and

adjusting, by one or more processors, the keywords based upon whether the number is below the minimum search results threshold or is above the maximum search results threshold.

6. The computer-implemented method of claim 1, wherein identifying the plurality of keywords includes:

identifying a plurality of subsets of labels from the identified plurality of labels, each subset containing no more than a predetermined maximum number of labels;

for each of the subsets of labels;

searching one or more vendor sites for occurrences of any of the labels in the subset; and

identifying a number of potential options associated with the occurrences of the labels; and

identifying the plurality of keywords by selecting one of the subsets of labels as the keywords based upon the number of potential options associated with the subset.

7. The computer-implemented method of claim 6, wherein the predetermined maximum number of labels is twenty.

8. The computer-implemented method of claim 1, wherein:

identifying the plurality of labels includes identifying at least one label by automatically recognizing text within the electronic image using optical character recognition (OCR) techniques; and

the at least one label identified by OCR techniques is limited to no more than twenty characters.

9. The computer-implemented method of claim 1, wherein identifying the plurality of labels includes identifying at least one label associated by automatically recognizing a logo represented within the electronic image.

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

receiving, at one or more processors, an indication of a user selection of one of the one or more options; and

communicating, by one or more processors, an order to a vendor server associated with the option indicated by the user selection to cause an order to be placed for the user to purchase the product associated with the option.

11. A computer system, comprising:

one or more processors;

a program memory storing executable instructions that, when executed by the one or more processors, cause the computer system to;

obtain an electronic image including a representation of an object within the image;

identify a plurality of labels associated with the image, wherein at least one of the labels is associated with the object;

identify a plurality of keywords associated with the object based upon the plurality of labels;

generate an option dataset containing a plurality of data entries indicating options based upon the plurality of keywords, each option being associated with acquisition of a product; and

cause one or more of the options indicated by the data entries to be presented to a user for review.

12. The computer system of claim 11, wherein the executable instructions that cause the computer system to identify the plurality of keywords further cause the computer system to:

identify a plurality of subsets of labels from the identified plurality of labels, each subset containing no more than a predetermined maximum number of labels;

for each of the subsets of labels;

search one or more vendor sites for occurrences of any of the labels in the subset; and

identify a number of potential options associated with the occurrences of the labels; and

identify the plurality of keywords by selecting one of the subsets of labels as the keywords based upon the number of potential options associated with the subset.

13. The computer system of claim 11, wherein the executable instructions that cause the computer system to identify the plurality of labels include executable instructions that cause the computer system to identify at least one label by automatically recognizing text within the electronic image using optical character recognition (OCR) techniques, the at least one label identified by OCR techniques being limited to no more than twenty characters.

14. The computer system of claim 11, wherein the executable instructions that cause the computer system to identify the plurality of labels include executable instructions that cause the computer system to identify at least one label associated by automatically recognizing a logo represented within the electronic image.

15. The computer system of claim 11, wherein the executable instructions further cause the computer system to:

receive an indication of a user selection of one of the one or more options; and

communicate an order to a vendor server associated with the option indicated by the user selection to cause an order to be placed for the user to purchase the product associated with the option.

16. A tangible, non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computer system, cause the computer system to:

obtain an electronic image including a representation of an object within the image;

identify a plurality of labels associated with the image, wherein at least one of the labels is associated with the object;

identify a plurality of keywords associated with the object based upon the plurality of labels;

generate an option dataset containing a plurality of data entries indicating options based upon the plurality of keywords, each option being associated with acquisition of a product; and

cause one or more of the options indicated by the data entries to be presented to a user for review.

17. The tangible, non-transitory computer-readable medium of claim 16, wherein the executable instructions that cause the computer system to identify the plurality of keywords include executable instructions that cause the computer system to:

identify a plurality of subsets of labels from the identified plurality of labels, each subset containing no more than a predetermined maximum number of labels;

for each of the subsets of labels;

search one or more vendor sites for occurrences of any of the labels in the subset; and

identify a number of potential options associated with the occurrences of the labels; and

identify the plurality of keywords by selecting one of the subsets of labels as the keywords based upon the number of potential options associated with the subset.

18. The tangible, non-transitory computer-readable medium of claim 16, wherein the executable instructions that cause the computer system to identify the plurality of labels include executable instructions that cause the computer system to identify at least one label by automatically recognizing text within the electronic image using optical character recognition (OCR) techniques, the at least one label identified by OCR techniques being limited to no more than twenty characters.

19. The tangible, non-transitory computer-readable medium of claim 16, wherein the executable instructions that cause the computer system to identify the plurality of labels include executable instructions that cause the computer system to identify at least one label associated by automatically recognizing a logo represented within the electronic image.

20. The tangible, non-transitory computer-readable medium of claim 16, further storing executable instructions that cause the computer system to:

receive an indication of a user selection of one of the one or more options; and

communicate an order to a vendor server associated with the option indicated by the user selection to cause an order to be placed for the user to purchase the product associated with the option.