US 11,688,023 B2
System and method of event processing with machine learning
Charles Howard Cella, Pembroke, MA (US)
Assigned to Strong Force TX Portfolio 2018, LLC, Fort Lauderdale, FL (US)
Filed by Strong Force TX Portfolio 2018, LLC, Santa Monica, CA (US)
Filed on Aug. 20, 2020, as Appl. No. 16/998,855.
Application 16/998,855 is a continuation of application No. 16/803,387, filed on Feb. 27, 2020.
Application 16/803,387 is a continuation of application No. PCT/US2019/058647, filed on Oct. 29, 2019.
Application 16/803,387 is a continuation in part of application No. PCT/US2019/030934, filed on May 6, 2019.
Application PCT/US2019/058647 is a continuation in part of application No. PCT/US2019/030934, filed on May 6, 2019.
Claims priority of provisional application 62/843,992, filed on May 6, 2019.
Claims priority of provisional application 62/843,455, filed on May 5, 2019.
Claims priority of provisional application 62/843,456, filed on May 5, 2019.
Claims priority of provisional application 62/818,100, filed on Mar. 13, 2019.
Claims priority of provisional application 62/787,206, filed on Dec. 31, 2018.
Claims priority of provisional application 62/751,713, filed on Oct. 29, 2018.
Claims priority of provisional application 62/667,550, filed on May 6, 2018.
Prior Publication US 2020/0387966 A1, Dec. 10, 2020
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/02 (2023.01); G06Q 20/10 (2012.01); G06Q 30/02 (2023.01); G06N 20/00 (2019.01); G06Q 30/00 (2023.01); G06Q 40/08 (2012.01); G06F 9/54 (2006.01); G06F 16/23 (2019.01); G06Q 50/18 (2012.01); G06Q 50/26 (2012.01); G06Q 50/00 (2012.01); G06Q 30/0208 (2023.01); G06Q 10/10 (2023.01); G06Q 30/0207 (2023.01); G06F 16/27 (2019.01); G06N 3/08 (2023.01); G06Q 30/0201 (2023.01); G06F 9/46 (2006.01); G06Q 10/0639 (2023.01); G06Q 20/40 (2012.01); H04L 9/06 (2006.01); G06N 5/04 (2023.01); G06Q 30/018 (2023.01); G16Y 10/50 (2020.01); G16Y 40/10 (2020.01); G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06F 18/241 (2023.01); G06Q 40/03 (2023.01); G06N 3/042 (2023.01); G06V 10/762 (2022.01); G06Q 40/04 (2012.01)
CPC G06Q 50/01 (2013.01) [G06F 9/466 (2013.01); G06F 9/543 (2013.01); G06F 16/2379 (2019.01); G06F 16/27 (2019.01); G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06F 18/241 (2023.01); G06N 3/042 (2023.01); G06N 3/08 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/0639 (2013.01); G06Q 10/10 (2013.01); G06Q 20/405 (2013.01); G06Q 30/018 (2013.01); G06Q 30/0201 (2013.01); G06Q 30/0206 (2013.01); G06Q 30/0208 (2013.01); G06Q 30/0215 (2013.01); G06Q 30/0278 (2013.01); G06Q 40/03 (2023.01); G06Q 40/08 (2013.01); G06Q 50/18 (2013.01); G06Q 50/188 (2013.01); G06Q 50/26 (2013.01); G06V 10/762 (2022.01); G16Y 10/50 (2020.01); G16Y 40/10 (2020.01); H04L 9/0637 (2013.01); G06Q 40/04 (2013.01); G06Q 2220/18 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A system for automatically managing an activity related to a transaction, comprising:
a set of processors providing an Internet of Things (IoT) data collection circuit that collects information about at least one entity involved in at least one transaction that includes at least one bond, maintains a first training data set that comprises a plurality of outcomes related to a set of entities involved in a set of previous transactions, and maintains a second training data set that comprises a plurality of bond management activities;
a condition classifying circuit that includes an artificial intelligence (AI) circuit and that automatically classifies at least one condition of the at least one entity in accordance with a model and based on information collected from the Internet of Things data collection circuit, wherein the AI circuit is trained using the first training data set;
an event processing circuit that automatically processes an event relevant to at least one of a value, a condition, or an ownership of at least one asset, and executes an action related to the at least one transaction in response to the automatically classified condition of the at least one entity; and
an automated bond management circuit that manages an action related to the at least one bond, wherein the automated bond management circuit is trained using the second training data set.