System and method for determination of a digital destination based on a multi-part identifier

Patent metadata

Field	Value
Publication number	US-10650072-B2
Application number	US-201715798179-A
Country	US
Kind code	B2
Filing date	Oct 30, 2017
Priority date	Oct 30, 2017
Publication date	May 12, 2020
Grant date	May 12, 2020

Abstract

One general aspect includes a method, including: capturing an image of an object having a multi-part identifier displayed thereon, the multi-part identifier including a first portion and a second portion, the first portion including graphical content and the second portion including human-recognizable textual content. The method also includes based on the captured image, identifying a domain associated with the graphical content. The method also includes based on the captured image, identifying a sub-part of the domain associated with the textual content. The method also includes identifying a digital destination based on the identified domain and the identified sub-part. The method also includes performing an action based on the digital destination. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

First claim

What is claimed is:

1. A method, comprising:
capturing an image of an object having a multi-part identifier displayed thereon, the multi-part identifier comprising a first portion and a second portion, the first portion comprising graphical content and the second portion comprising human-recognizable textual content;
identifying a domain based on the graphical content, wherein the domain represents a network having a plurality of network addresses;
identifying a sub-part of the domain based on the textual content, wherein the sub-part of the domain represents a particular network address in the plurality of network addresses;
identifying a digital destination based on the identified domain and the identified sub-part, the digital destination corresponding to the particular network address represented by the sub-part of the domain; and
performing an action based on the digital destination.

2. The method of claim 1 , wherein performing the action comprises displaying content associated with the digital destination.

3. The method of claim 1 , wherein performing the action comprises executing an application based on data stored at the digital destination.

4. The method of claim 1 , wherein identifying the sub-part of the domain based on the textual content comprises decoding the textual content.

5. The method of claim 1 , wherein identifying the domain based on the graphical content comprises providing the graphical content as an input to a machine learning model, wherein, in response to the input, the machine learning model outputs a class identifying the domain.

6. The method of claim 5 , wherein the machine learning model is a convolutional neural network (CNN).

7. The method of claim 1 , wherein the object comprises at least one of a poster, billboard, sign, handwritten content, digital content, or receipt.

8. A system, comprising:
a processor; and
a non-transitory computer readable medium coupled to the processor, the computer readable medium comprising code, executable by the processor, for implementing a method comprising:
capturing an image of an object having a multi-part identifier displayed thereon, the multi-part identifier comprising a first portion and a second portion, the first portion comprising graphical content and the second portion comprising human-recognizable textual content;
identifying a domain based on the graphical content, wherein the domain represents a network having a plurality of network addresses;
identifying a sub-part of the domain based on the textual content, wherein the sub-part of the domain represents a particular network address in the plurality of network addresses;
identifying a digital destination based on the identified domain and the identified sub-part, the digital destination corresponding to the particular network address represented by the sub-part of the domain; and
performing an action based on the digital destination.

9. The system of claim 8 , wherein performing the action comprises displaying content associated with the digital destination.

10. The system of claim 8 , wherein performing the action comprises executing an application based on data stored at the digital destination.

11. The system of claim 8 , wherein identifying the sub-part of the domain based on the textual content comprises decoding the textual content.

12. The system of claim 8 , wherein identifying the domain based on the graphical content comprises providing the graphical content as an input to a machine learning model, wherein, in response to the input, the machine learning model outputs a class identifying the domain.

13. The system of claim 12 , wherein the machine learning model is a convolutional neural network (CNN).

14. The system of claim 8 , wherein the object comprises at least one of a poster, billboard, sign, handwritten content, digital content, or receipt.

15. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause one or more computing devices to:
capture an image of an object having a multi-part identifier displayed thereon, the multi-part identifier comprising a first portion and a second portion, the first portion comprising graphical content and the second portion comprising human-recognizable textual content;
identify a domain based on the graphical content, wherein the domain represents a network having a plurality of network addresses;
identify a sub-part of the domain based on the textual content, wherein the sub-part of the domain represents a particular network address in the plurality of network addresses;
identify a digital destination based on the identified domain and the identified sub-part, the digital destination corresponding to the particular network address represented by the sub-part of the domain; and
perform an action based on the digital destination.

16. The one or more non-transitory computer-readable media of claim 15 , wherein performing the action comprises at least one of displaying content associated with the digital destination or executing an application based on data stored at the digital destination.

17. The one or more non-transitory computer-readable media of claim 15 , wherein identifying the sub-part of the domain based on the textual content comprises decoding the textual content.

18. The one or more non-transitory computer-readable media of claim 15 , wherein identifying the domain based on the graphical content comprises providing the graphical content as an input to a machine learning model, wherein, in response to the input, the machine learning model outputs a class identifying the domain.

19. The one or more non-transitory computer-readable media of claim 18 , wherein the machine learning model is a convolutional neural network (CNN).

20. The one or more non-transitory computer-readable media of claim 15 , wherein the object comprises at least one of a poster, billboard, sign, handwritten content, digital content, or receipt.

Assignees

• Facebook Inc

Inventors

• Loveva Maria
• Canton Matthew William
• Zhang Peizhao
• Wei Shihang
• Wang Shen
• Vajda Peter
• Wang Han

Classifications

• G06N3/08

  Learning methods · CPC title

• G06F16/9554 Primary

  by using bar codes · CPC title

• G06K7/1417

  2D bar codes · CPC title

• G06K2209/21

  Physics · mapped topic

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title