The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 06, 2025

Filed:

May. 03, 2024
Applicant:

Citibank, N.a., New York, NY (US);

Inventors:

Shubhanshu Gupta, Singapore, SG;

Ashish Awasthi, Singapore, SG;

Amaruvi Devanathan, Chennai, IN;

Mallapu Raghavulu Surya Prakash, Singapore, SG;

Assignee:

CITIBANK, N.A., New York, NY (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 21/62 (2012.12); G06F 16/22 (2018.12); G06F 16/334 (2024.12); G06F 16/335 (2018.12);
U.S. Cl.
CPC ...
G06F 21/6254 (2012.12); G06F 16/221 (2018.12); G06F 16/3347 (2018.12); G06F 16/335 (2018.12);
Abstract

Methods and systems herein identify and redact personally identifiable information. A PII sensitivity detection framework includes multiple layers where each layer corresponds to a computer model. The framework analyzes data stored within different data tables and predicts whether a data column includes PII. The first layer corresponds to an artificial intelligence model that analyzes each column metadata and predicts a first score indicative of a likelihood of PII. The second layer corresponds to a rule-based computer model that uses various rules to determine a second score indicative of a likelihood of PII for each column. The third layer corresponds to a column content model that analyzes content of each column using various natural language processing techniques to generate a third score indicative of a likelihood of PII. The framework masks data being presented to a user based on the scores generated via execution of one or more of the layers.


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