IP Library Granted Patent US 12,401,650
Granted Patent B1
US 12,401,650 · App. 17/903,835 · Granted Aug 26, 2025

Secure digital authorization based on identity elements of users and/or linkage definitions identifying shared digital assets

Inventors: Thomas E. Bell (San Francisco, CA); Peter Bordow (Fountain Hills, AZ); Julio Jiron (San Bruno, CA); Akhlaq M. Khan (San Francisco, CA); Volkmar Scharf-Katz (San Francisco, CA); Jeff J. Stapleton (Arlington, TX); Richard Orlando Toohey (San Francisco, CA); Ramesh Yarlagadda (San Francisco, CA)
Assignee: Wells Fargo Bank, N.A.
H04L63/102G06F21/6218H04L63/0853
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 12,401,650
App. No.
17/903,835
Granted
Aug 26, 2025
Kind
B1
Abstract

Disclosed are example methods, systems, and devices that allow for secure digital authorization via generated datasets. The techniques include receiving a first dataset of a first user and a second dataset of a second user and generating a first set of identity elements and a second set of identity elements based on the first dataset and the second dataset, respectively. A linkage definition can be generated based on the first and second datasets, which can be associated with a set of activation elements. The techniques include determining that a set of inputs satisfy one or more of the set of activation elements and, in response, generating a set of security access tokens based on the linkage condition. The security access tokens can be transmitted to a computing device upon analyzing and verifying biometric data received from that computing device.

Claims (39)

1. A method comprising:

retrieving, by a computing system comprising one or more processors, from a first digital identity profile of a first entity, a first set of identity elements and a first set of metadata corresponding to the first set of identity elements;

retrieving, by the computing system, from a second digital identity profile of a second entity, a second set of identity elements and a second set of metadata corresponding to the second set of identity elements;

generating, by the computing system, a dataset based on a plurality of the first set of identity elements, the second set of identity elements, the first set of metadata, and the second set of metadata;

inputting, by the computing system, the dataset to an artificial intelligence (AI) agent to generate a linkage definition and a set of activation elements, the AI agent having been trained by applying one or more machine learning models to a set of session logs corresponding to digital identity profiles of a plurality of linked entities, wherein the linkage definition identifies one or more physical or digital assets of one or both of the first entity or the second entity, and wherein the set of activation elements identifies one or more states;

receiving, by the computing system, from a plurality of computing devices, a set of inputs corresponding to the first entity and the second entity;

determining, by the computing system, based on the set of inputs, that the set of activation elements has been triggered;

in response to determining that the set of activation elements has been triggered, generating, by the computing system, a set of one or more security access tokens based on the linkage definition, the set of one or more security access tokens indicating that access to select digital or physical assets are granted for specified time periods; and

transmitting, by the computing system, the set of one or more security access tokens to at least one of a first device identified in the first digital identity profile or a second device identified in the second digital identity profile.

2. The method of claim 1 , wherein applying the one or more machine learning models comprises applying a pattern recognition model or a classification model to recognize normal or abnormal patterns of behavior.

3. The method of claim 1 , wherein applying the one or more machine learning models comprises applying a regression model to identify causal factors for one or more identity elements or corresponding metadata in digital identity profiles.

4. The method of claim 1 , wherein applying the one or more machine learning models comprises applying a decisioning model to identify actions suited to achieving particular goals based on available options.

5. The method of claim 1 , further comprising adding, by the computing system, the set of one or more security access tokens to at least one of the first digital identity profile or the second digital identity profile.

6. The method of claim 1 , wherein the set of one or more security access tokens grants access to one or more digital files.

7. The method of claim 1 , wherein the set of one or more security access tokens grants access to one or more smart devices.

8. The method of claim 1 , wherein the set of one or more security access tokens grants access to one or more physical locations.

9. The method of claim 1 , wherein the set of one or more security access tokens grants access to one or more articles of manufacture.

10. The method of claim 1 , wherein the first set of identity elements and the first set of metadata are received from a first computing system with the first digital identity profile, and the second set of identity elements and the second set of metadata are received from a second computing system with the second digital identity profile.

11. The method of claim 10 , wherein retrieving the first set of identity elements and the first set of metadata comprises transmitting a first application programming interface (API) call to the first computing system, and retrieving the second set of identity elements and the second set of metadata comprises transmitting a second API call to the second computing system.

12. The method of claim 1 , wherein the first digital identity profile and the second digital identity profile are maintained by the computing system.

13. The method of claim 1 , wherein the one or more states are based on one or more geophysical locations of at least one of the first device or the second device.

14. The method of claim 1 , wherein the plurality of computing devices comprises the first device and the second device.

15. The method of claim 1 , wherein the plurality of computing devices comprises one or more devices other than the first device and the second device.

16. The method of claim 1 , further comprising adding, by the computing system, the linkage definition and the set of activation elements to both the first digital identity profile and the second digital identity profile.

17. A computing system comprising one or more hardware processors coupled to a non-transitory memory, the computing system configured to:

retrieve, from a first digital identity profile of a first entity, a first set of identity elements and a first set of metadata corresponding to the first set of identity elements;

retrieve, from a second digital identity profile of a second entity, a second set of identity elements and a second set of metadata corresponding to the second set of identity elements;

generate a dataset based on a plurality of the first set of identity elements, the second set of identity elements, the first set of metadata, and the second set of metadata;

input the dataset to an artificial intelligence (AI) agent to generate a linkage definition and a set of activation elements, the AI agent having been trained by applying one or more machine learning models to a set of session logs corresponding to digital identity profiles of a plurality of linked entities, wherein the linkage definition identifies one or more physical or digital assets of one or both of the first entity or the second entity, and wherein the set of activation elements identifies one or more states;

receive, from a plurality of computing devices, a set of inputs corresponding to the first entity and the second entity;

determine, based on the set of inputs, that the set of activation elements has been triggered;

in response to determining that the set of activation elements has been triggered, generate a set of one or more security access tokens based on the linkage definition; and

transmit the set of one or more security access tokens to at least one of a first device identified in the first digital identity profile or a second device identified in the second digital identity profile to grant access to select digital or physical assets for specified time periods.

18. The computing system of claim 17 , wherein applying the one or more machine learning models comprises applying at least one of:

a pattern recognition model or a classification model to recognize normal or abnormal patterns of behavior;

a regression model to identify causal factors for one or more identity elements or corresponding metadata in digital identity profiles; or

a decisioning model to identify actions suited to achieving particular goals based on available options.

19. The computing system of claim 17 , wherein the set of one or more security access tokens grants access to at least one of a digital file, a smart device, a physical location, or an article of manufacture.

20. The computing system of claim 17 , wherein the first set of identity elements and the first set of metadata are received from a first computing system with the first digital identity profile, wherein the second set of identity elements and the second set of metadata are received from a second computing system with the second digital identity profile, wherein retrieving the first set of identity elements and the first set of metadata comprises transmitting a first application programming interface (API) call to the first computing system, and wherein retrieving the second set of identity elements and the second set of metadata comprises transmitting a second API call to the second computing system.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 8, 2025
From: BELL, THOMAS E.; BORDOW, PETER; JIRON, JULIO; KHAN, AKHLAQ M.; SCHARF-KATZ, VOLKMAR; STAPLETON, JEFF J.; TOOHEY, RICHARD ORLANDO; YARLAGADDA, RAMESH
To: WELLS FARGO BANK, N.A.
Reel/Frame 070767/0512 →
Continuity (1)
Continuation 17901550 · Sep 1, 2022
References Cited (122)
US 7133846B1 · Ginter et al. · 2006 [cited by applicant]
US 7673797B2 · Edwards · 2010 [cited by applicant]
US 8234387B2 · Bradley et al. · 2012 [cited by applicant]
US 8446275B2 · Utter, II · 2013 [cited by applicant]
US 8731977B1 · Hardin et al. · 2014 [cited by applicant]
US 8756153B1 · Rolf · 2014 [cited by applicant]
US 8831972B2 · Angell et al. · 2014 [cited by applicant]
US 8965803B2 · Jung et al. · 2015 [cited by applicant]
US 9087058B2 · Neven et al. · 2015 [cited by applicant]
US 9094388B2 · Tkachev · 2015 [cited by applicant]
US 9177257B2 · Kozloski et al. · 2015 [cited by applicant]
US 9443298B2 · Ross et al. · 2016 [cited by applicant]
US 9519783B2 · Pruthi et al. · 2016 [cited by applicant]
US 9558397B2 · Liu et al. · 2017 [cited by applicant]
US 9734290B2 · Srinivas et al. · 2017 [cited by applicant]
US 9864992B1 · Robinson et al. · 2018 [cited by applicant]
US 10024684B2 · Wang · 2018 [cited by applicant]
US 10044700B2 · Gresham et al. · 2018 [cited by applicant]
US 10075445B2 · Chen et al. · 2018 [cited by applicant]
US 10102491B2 · Connolly et al. · 2018 [cited by applicant]
US 10110608B2 · Dureau · 2018 [cited by applicant]
US 10127378B2 · Toth · 2018 [cited by applicant]
US 10142362B2 · Weith et al. · 2018 [cited by applicant]
US 10181032B1 · Sadaghiani et al. · 2019 [cited by applicant]
US 10210527B2 · Radocchia · 2019 [cited by applicant]
US 10313336B2 · Giobbi · 2019 [cited by applicant]
US 10362027B2 · Eramian et al. · 2019 [cited by applicant]
US 10387695B2 · Engels et al. · 2019 [cited by applicant]
US 10505965B2 · Moyle et al. · 2019 [cited by applicant]
US 10552596B2 · Wang et al. · 2020 [cited by applicant]
US 10572778B1 · Robinson et al. · 2020 [cited by applicant]
US 10614302B2 · Withrow · 2020 [cited by applicant]
US 10664581B2 · Hou et al. · 2020 [cited by applicant]
US 10740767B2 · Withrow · 2020 [cited by applicant]
US 10757097B2 · Yocam et al. · 2020 [cited by applicant]
US 10778676B1 · Griffin et al. · 2020 [cited by applicant]
US 10834084B2 · Ouellette et al. · 2020 [cited by applicant]
US 10855679B2 · Rajakumar · 2020 [cited by applicant]
US 10938828B1 · Badawy et al. · 2021 [cited by applicant]
US 10943003B2 · Bingham et al. · 2021 [cited by applicant]
US 10963670B2 · Ross et al. · 2021 [cited by applicant]
US 10977353B2 · Bender et al. · 2021 [cited by applicant]
US 11044267B2 · Jakobsson et al. · 2021 [cited by applicant]
US 11048794B1 · Bordow · 2021 [cited by applicant]
US 11048894B2 · Feldman · 2021 [cited by applicant]
US 11055390B1 · Kragh · 2021 [cited by applicant]
US 11057366B2 · Avetisov et al. · 2021 [cited by applicant]
US 11068909B1 · Land et al. · 2021 [cited by applicant]
US 11075904B2 · Jha et al. · 2021 [cited by applicant]
US 11089014B2 · Buscemi · 2021 [cited by applicant]
US 11093789B2 · Wang et al. · 2021 [cited by applicant]
US 11127092B2 · Kurian · 2021 [cited by applicant]
US 11128467B2 · Chapman et al. · 2021 [cited by applicant]
US 11151550B2 · Prabhu et al. · 2021 [cited by applicant]
US 11157907B1 · Kumar · 2021 [cited by applicant]
US 11163931B2 · Ricci · 2021 [cited by applicant]
US 11200306B1 · Singh · 2021 [cited by applicant]
US 11205011B2 · Jakobsson et al. · 2021 [cited by applicant]
US 11223646B2 · Cunningham et al. · 2022 [cited by applicant]
US 11290448B1 · Bordow · 2022 [cited by applicant]
US 11327992B1 · Batsakis et al. · 2022 [cited by applicant]
US 11451532B2 · Arif Khan et al. · 2022 [cited by applicant]
US 11514155B1 · Bordow · 2022 [cited by applicant]
US 11522867B2 · Han et al. · 2022 [cited by applicant]
US 11669611B1 · Bordow · 2023 [cited by applicant]
US 20030086341A1 · Wells et al. · 2003 [cited by applicant]
US 20060129478A1 · Rees · 2006 [cited by applicant]
US 20070078908A1 · Rohatgi et al. · 2007 [cited by applicant]
US 20080022370A1 · Beedubail et al. · 2008 [cited by applicant]
US 20080120302A1 · Thompson · 2008 [cited by applicant]
US 20090089107A1 · Angell et al. · 2009 [cited by applicant]
US 20090089205A1 · Bayne · 2009 [cited by applicant]
US 20120237908A1 · Fitzgerald et al. · 2012 [cited by applicant]
US 20150112732A1 · Trakru et al. · 2015 [cited by applicant]
US 20150220999A1 · Thornton et al. · 2015 [cited by applicant]
US 20150317728A1 · Nguyen · 2015 [cited by applicant]
US 20160050557A1 · Park et al. · 2016 [cited by applicant]
US 20160162882A1 · Mcclung, III · 2016 [cited by applicant]
US 20160224773A1 · Ramaci · 2016 [cited by applicant]
US 20160335629A1 · Scott · 2016 [cited by applicant]
US 20170012992A1 · Doctor et al. · 2017 [cited by applicant]
US 20170063831A1 · Arnold et al. · 2017 [cited by applicant]
US 20170063946A1 · Quan et al. · 2017 [cited by applicant]
US 20170111351A1 · Grajek et al. · 2017 [cited by applicant]
US 20170230351A1 · Hallenborg · 2017 [cited by applicant]
US 20170236037A1 · Rhoads et al. · 2017 [cited by applicant]
US 20180205546A1 · Haque et al. · 2018 [cited by applicant]
US 20190095916A1 · Jackson · 2019 [cited by applicant]
US 20190149539A1 · Scruby · 2019 [cited by applicant]
US 20190163889A1 · Bouse · 2019 [cited by applicant]
US 20190205939A1 · Lal et al. · 2019 [cited by applicant]
US 20190334724A1 · Anton et al. · 2019 [cited by applicant]
US 20200211031A1 · Patil · 2020 [cited by applicant]
US 20200266985A1 · Covaci et al. · 2020 [cited by applicant]
US 20200311678A1 · Fletcher et al. · 2020 [cited by applicant]
US 20200374311A1 · Madhu et al. · 2020 [cited by applicant]
US 20200380598A1 · Spector et al. · 2020 [cited by applicant]
US 20210027061A1 · Xu et al. · 2021 [cited by applicant]
US 20210089637A1 · Cummins et al. · 2021 [cited by applicant]
US 20210104008A1 · Ross et al. · 2021 [cited by applicant]
US 20210110004A1 · Ross et al. · 2021 [cited by applicant]
US 20210134434A1 · Riley et al. · 2021 [cited by applicant]
US 20210202067A1 · Williams et al. · 2021 [cited by applicant]
US 20210240837A1 · Tseng et al. · 2021 [cited by applicant]
US 20210258155A1 · Andon et al. · 2021 [cited by applicant]
US 20210279475A1 · Tusch et al. · 2021 [cited by applicant]
US 20210326467A1 · Levy et al. · 2021 [cited by applicant]
US 20210366014A1 · Wang et al. · 2021 [cited by applicant]
US 20210366586A1 · Ryan et al. · 2021 [cited by applicant]
US 20220292396A1 · Biryukov et al. · 2022 [cited by applicant]
US 20240064135A1 · Sherlock et al. · 2024 [cited by applicant]
US 20240185596A1 · Neuschãet al. · 2024 [cited by applicant]
US 20240214194A1 · Kapur et al. · 2024 [cited by applicant]
US 20240340314A1 · Radon et al. · 2024 [cited by applicant]
CA 2478548C · 2014 [cited by applicant]
DE 102021108925A1 · 2022 [cited by applicant]
WO WO2011016710A1 · 2011 [cited by applicant]
WO WO2016083987A1 · 2016 [cited by applicant]
WO WO2019013818A1 · 2019 [cited by examiner]
WO WO2019123291A1 · 2019 [cited by applicant]
Jain, et al., A Blockchain-Based distributed network for Secure Credit Scoring, 2019 5th International Conference on Signal Processing, Computing and Control (ISPCC), 306-12, Oct. 2019; ISBN-13: 978-1-7281-3988-3. [cited by applicant]
Yan Zhang et al., Real-time Machine Learning Prediction of an Agent-Based Model for Urban Decision-making, URL: https://ifaamas.org/Proceedings/aamas2018/pdfs/p2171.pdf (Jul. 10-15, 2018). [cited by applicant]
Cited By (1)
US 12,645,819