View source for 18543922. SYSTEMS AND METHODS OF PHOTO-BASED FRAUD PROTECTION simplified abstract (Capital One Services, LLC)

=SYSTEMS AND METHODS OF PHOTO-BASED FRAUD PROTECTION=


==Organization Name==

[[:Category:Capital One Services, LLC|Capital One Services, LLC]]


[[Category:Capital One Services, LLC]]

==Inventor(s)==

[[:Category:Michael Mossoba of Great Falls VA (US)|Michael Mossoba of Great Falls VA (US)]][[Category:Michael Mossoba of Great Falls VA (US)]]

[[:Category:Joshua Edwards of Philadelphia PA (US)|Joshua Edwards of Philadelphia PA (US)]][[Category:Joshua Edwards of Philadelphia PA (US)]]

[[:Category:Sarah Cunningham of Arlington VA (US)|Sarah Cunningham of Arlington VA (US)]][[Category:Sarah Cunningham of Arlington VA (US)]]

[[:Category:Mark Rothbaum of Washington DC (US)|Mark Rothbaum of Washington DC (US)]][[Category:Mark Rothbaum of Washington DC (US)]]

==SYSTEMS AND METHODS OF PHOTO-BASED FRAUD PROTECTION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18543922 titled 'SYSTEMS AND METHODS OF PHOTO-BASED FRAUD PROTECTION

==Simplified Explanation==

The computer-implemented method of fraud protection described in the abstract involves receiving keywords and a likelihood of fraud, extracting text from an image of a document, comparing the text to stored keywords, and calculating a confidence level of fraud to send back to the user device.

* Keywords and likelihood of fraud received from an external source
* Image of a document received from a user device
* Text extracted from the document image
* Comparison of extracted text to stored keywords
* Calculation of confidence level of fraud
* Indication of confidence level sent to user device

== Potential Applications ==
This technology could be applied in various industries such as banking, insurance, and e-commerce to detect fraudulent activities and protect against financial losses.

== Problems Solved ==
This technology helps in identifying potential fraud by analyzing text data from documents, thereby reducing the risk of financial fraud and improving security measures for businesses and individuals.

== Benefits ==
The benefits of this technology include enhanced fraud detection capabilities, improved security measures, reduced financial losses, and increased trust and confidence in online transactions.

== Potential Commercial Applications ==
The potential commercial applications of this technology include fraud detection software for financial institutions, identity verification services for online platforms, and security solutions for e-commerce websites.

== Possible Prior Art ==
One possible prior art for this technology could be existing fraud detection systems used in financial institutions and online platforms, which may involve similar processes of analyzing text data for fraud prevention.

=== Unanswered Questions ===

=== How does the technology handle different languages in the extracted text data? ===
The abstract does not specify how the technology deals with text data in multiple languages. It would be important to understand if the system has language detection capabilities or if it relies on a specific language for fraud detection.

=== What is the accuracy rate of the fraud detection system based on the confidence level calculated? ===
The abstract does not mention the accuracy rate of the fraud detection system. It would be crucial to know how reliable the system is in detecting fraud based on the confidence level calculated.


==Original Abstract Submitted==

A computer-implemented method of fraud protection is described. A server may receive one or more keywords and a likelihood of fraud from an external source. The server may receive an image of a document from a user device, wherein the document comprises at least one of a written communication or a printed communication. The server may extract text from the image of the document, compare the extracted text to the one or more stored keywords, and calculate a confidence level of fraud. The server may send an indication of the confidence level of fraud to the user device.



[[Category:G06Q30/018]]
[[Category:G06F16/903]]
[[Category:G06V30/413]]