IP Library Granted Patent US 12,603,873
Granted Patent B2
US 12,603,873 · App. 18/620,449 · Granted Apr 14, 2026

Dynamic one-time use knowledge-based authentication via multi-sourced private data using artificial intelligence techniques

Inventors: Agasthya P. Narendranathan (San Ramon, CA); James M. Dzierzanowski (Gilbert, AZ)
Assignee: MATRIXED IP HOLDINGS, LLC
H04L63/08G06F21/31G06F40/279G06F40/40
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,603,873
App. No.
18/620,449
Granted
Apr 14, 2026
Kind
B2
Abstract

Exemplary systems and methods utilize unique knowledge-based authentication techniques involving private and/or recent data. Via use of the disclosed concepts, authentication is made more robust, and is hardened against compromise techniques such as those drawing from prior data breaches, public records, and the like. In this manner, simpler and more reliable authentication is achieved.

Claims (38)

1 . A computer-implemented method for passwordless authentication, comprising:

receiving, by an authentication system, a private corpus of data associated with a user;

processing, by the authentication system, the private corpus to initiate generation of a set of challenge questions;

receiving, by the authentication system and over a network, a request for authentication from a user device associated with the user;

reading a first record from the private corpus of data, in response to receiving the request for authentication;

generating an intermediate semantic representation that associates conceptual knowledge with linguistic expressions from the first record;

generating distractor content to differ semantically while remaining contextually plausible;

generating distractor sentences using concepts drawn from different knowledge domains;

using pre-identified tags, a genre table for the data set and semantic representation, for constructing a human-readable sentence with natural-language statements based on the first record,

wherein the genre table determines vocabulary selection, sentence form and narrative framing for constructing the human-readable sentence that is contextually appropriate;

maintaining domain-specific linguistic structures defining permissible vocabulary, sentence forms, and conceptual groupings used during content generation;

repeating the reading and constructing steps until a plurality of human-readable sentences are constructed for each of the records in the private corpus of data;

evaluating the generated content against domain rules to ensure consistency with intended subject matter prior to presentation,

wherein the plurality of the human-readable sentences form a set of challenge questions;

delivering, by the authentication system and to the user device via the network, a first challenge question from the set of challenge questions;

responsive to receiving, by the authentication system and over the network and from the user device, the correct answer to the first challenge question, authenticating the user; and

granting, by the authentication system, access to at least one of a secure computer, application or website.

2 . The method of claim 1 , wherein the constructing the human-readable sentence based on the first record uses at least one of pre-defined tags, multiple choice questions (MCQ) ontology or a genre configuration table that governs how questions are at least one of selected, clustered or themed.

3 . The method of claim 2 , wherein the private corpus of data comprises data regarding events taking place within a predetermined time frame prior to the receiving.

4 . The method of claim 3 , wherein the predetermined time frame is less than one week, or less than two weeks, or less than one month.

5 . The method of claim 4 , wherein the private corpus of data comprises at least one of credit card transactions for a credit card of the user, geolocation data for the user device, healthcare records associated with the user, or calendar appointment information for the user.

6 . The method of claim 2 , further comprising, responsive to the authenticating, at least one of deleting the first challenge question from the set of challenge questions or flagging the first challenge question to prevent re-use.

7 . The method of claim 1 , wherein the first challenge question comprises a zero-knowledge proof.

8 . The method of claim 1 , wherein the constructing the human-readable sentence based on the first record uses at least one of the genre table or a distractor look up table.

9 . The method of claim 1 , further comprising:

responsive to receiving, by the authentication system and over the network and from the user device, an incorrect answer to the first challenge question, delivering to the user device, via the network, a second challenge question from the set of challenge questions.

10 . The method of claim 9 , wherein the second challenge question is based on more data from the private corpus of data than the first challenge question.

11 . The method of claim 10 , further comprising flagging, by the authentication system and in the set of challenge questions, the first challenge question and the second challenge question to prevent reuse of the first challenge question and the second challenge question.

12 . The method of claim 11 , further comprising deleting, by the authentication system, the flagged challenge questions from the set of challenge questions.

13 . The method of claim 1 , wherein the first challenge question is a multiple-choice question.

14 . The method of claim 1 , further comprising:

receiving, by the authentication system and over the network, update data associated with the user;

adding, by the authentication system, the update data to the private corpus of data;

processing, by the authentication system, the update data to generate additional challenge questions; and

adding, by the authentication system, the additional challenge questions to the set of challenge questions.

15 . The method of claim 1 , further comprising annotating, by the authentication system and in the set of challenge questions, each challenge question with a use-by date by which the challenge question should be used or discarded.

16 . The method of claim 15 , wherein each record in the private corpus of data has a generation date, and wherein the use-by date is a predetermined number of days from the generation date of the record in the private corpus from which the associated challenge question was generated.

17 . The method of claim 1 , further comprising deleting, by the authentication system and from the set of challenge questions, at least one challenge question having a use-by date prior to a current date.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 28, 2024
From: NARENDRANATHAN, AGASTHYA P.; DZIERZANOWSKI, JAMES M.
To: MATRIXED IP HOLDINGS, LLC
Reel/Frame 066939/0155 →
Continuity (2)
Provisional Application 63455365 · Mar 29, 2023
Related Publication 20240333699A1 · Oct 3, 2024
References Cited (39)
US 8104084B2 · Smithson · 2012 [cited by examiner]
US 8112817B2 · Chiruvolu · 2012 [cited by examiner]
US 8136148B1 · Chayanam · 2012 [cited by examiner]
US 8375420B2 · Farrell · 2013 [cited by examiner]
US 8380629B2 · Carlson · 2013 [cited by examiner]
US 8396926B1 · Oliver · 2013 [cited by examiner]
US 8533118B2 · Weller · 2013 [cited by examiner]
US 8677466B1 · Chuang et al. · 2014 [cited by applicant]
US 8732089B1 · Fang · 2014 [cited by examiner]
US 9323930B1 · Satish · 2016 [cited by examiner]
US 9591035B2 · Kuo · 2017 [cited by examiner]
US 9794228B2 · Li · 2017 [cited by examiner]
US 9813402B1 · Chen · 2017 [cited by examiner]
US 9898740B2 · Weller · 2018 [cited by examiner]
US 9928358B2 · Ghosh · 2018 [cited by examiner]
US 10452826B2 · Rush · 2019 [cited by examiner]
US 10999734B1 · Alexander · 2021 [cited by examiner]
US 11062014B1 · Raman · 2021 [cited by examiner]
US 11068891B2 · Ghosh · 2021 [cited by examiner]
US 12107972B2 · Mokhasi · 2024 [cited by examiner]
US 20050268107A1 · Harris et al. · 2005 [cited by applicant]
US 20060165060A1 · Dua · 2006 [cited by applicant]
US 20090259848A1 · Williams et al. · 2009 [cited by applicant]
US 20100121867A1 · Gosejacob · 2010 [cited by examiner]
US 20110302644A1 · Headley · 2011 [cited by applicant]
US 20120124651A1 · Ganesan et al. · 2012 [cited by applicant]
US 20140137219A1 · Castro · 2014 [cited by examiner]
US 20150106216A1 · Kenderov · 2015 [cited by examiner]
US 20150188898A1 · Chow · 2015 [cited by examiner]
US 20150249540A1 · Khalil · 2015 [cited by examiner]
US 20160380941A1 · Tanurdjaja · 2016 [cited by examiner]
US 20180048634A1 · Fang · 2018 [cited by examiner]
US 20200327432A1 · Doebelin · 2020 [cited by examiner]
US 20210406444A1 · Vontobel · 2021 [cited by examiner]
US 20240333699A1 · Narendranathan · 2024 [cited by examiner]
Nwafor et al., “An Automated Multiple-Choice Question Generation using Natural Language Processing Techniques”, International Journal on Natural Language Computing, https://arxiv.org/pdf/2103.14757, Apr. 2021, pp. 1-11. [cited by applicant]
Mehta et al., “Automated MCQ generator using natural language processing.” Internation Research Journal of Engineering and Technology 8, https://www.irjet.net/archives/V8/15/IRJET-V815497.pdf, May 2021, pp. 2705-2710. [cited by applicant]
Madri et al., “A comprehensive review on MCQ generation from text”, Multimed Tools Appl 82, https://doi.org/10.1007/s11042-023-14768-5, Oct. 2023, pp. 39415-39434. [cited by applicant]
Das et al., “Multiple-choice question generation with auto-generated distractors for computer-assisted educational assessment”, Multimed Tools Appl 80, https://doi.org/10.1007/s11042-021-11222-2, Sep. 2021, pp. 31907-31… [cited by applicant]