US 12,248,938 B2
Systems and methods for blockchain based identity assurance and risk management
Agasthya P. Narendranathan, San Ramon, CA (US); and James M. Dzierzanowski, Gilbert, AZ (US)
Assigned to NEUROSYMBOLIC AI-IP, LLC, Gilbert, AZ (US)
Filed by NEUROSYMBOLIC AI-IP, LLC, Gilbert, AZ (US)
Filed on Mar. 14, 2022, as Appl. No. 17/693,766.
Application 17/693,766 is a continuation of application No. 16/846,677, filed on Apr. 13, 2020, granted, now 11,321,718.
Application 16/846,677 is a continuation of application No. 16/037,986, filed on Jul. 17, 2018, abandoned.
Claims priority of provisional application 62/533,241, filed on Jul. 17, 2017.
Claims priority of provisional application 62/641,905, filed on Mar. 12, 2018.
Prior Publication US 2022/0198458 A1, Jun. 23, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 21/60 (2013.01); G06F 21/62 (2013.01); G06Q 20/40 (2012.01); H04L 9/00 (2022.01)
CPC G06Q 20/4016 (2013.01) [G06F 21/602 (2013.01); G06F 21/6245 (2013.01); H04L 9/50 (2022.05)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, by a processor, a first identity attribute;
determining, by the processor, a corresponding first Level of Assurance (LOA) based on the first identity attribute and a first attribute history;
receiving, by the processor, a second identity attribute;
determining, by the processor, a corresponding second LOA based on the second identity attribute and a second attribute history;
defining, by the processor, a Self-Sovereign Identity (SSI) based on the first identity attribute, the second identity attribute, the first LOA, and the second LOA;
receiving, by the processor, raw data comprising at least one of internet protocol data, device fingerprint data, browser fingerprint data, unique user data, session ID data, local time, remote time, or transaction data;
applying, by the processor, predictive modeling techniques to the raw data;
generating, by the processor, a predictive data set based on the applied predictive modeling techniques;
determining, by the processor, a threshold for a threshold based action, based on a machine learning technique applied to the predictive data set and feedback inputs including at the least one of the internet protocol data, the device fingerprint data, the browser fingerprint data, the unique user data, the session ID data, the local time, the remote time, or the transaction data; and
performing, by the processor, the threshold based action, wherein the threshold based action includes at least one of an alert, a challenge, a block, or a lock.