US 11,934,925 B2
Creating a machine learning policy based on express indicators
Daniel Joseph Potkalesky, Garland, TX (US); and Mark Stephen DeMichele, Ann Arbor, MI (US)
Assigned to ZixCorp Systems, Inc., Dallas, TX (US)
Filed by ZixCorp Systems, Inc., Dallas, TX (US)
Filed on Apr. 14, 2022, as Appl. No. 17/720,737.
Application 17/720,737 is a continuation of application No. 16/194,532, filed on Nov. 19, 2018, granted, now 11,341,430.
Prior Publication US 2022/0237517 A1, Jul. 28, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 20/00 (2019.01); G06F 21/60 (2013.01)
CPC G06N 20/00 (2019.01) [G06F 21/602 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A method for creating a machine learning policy based on express indicators comprising:
receiving, by a scanner, at least one electronic message, the at least one electronic message outbound from at least one sender to at least one recipient;
determining, by the scanner, at least one classification type that applies to the at least one electronic message, the at least one classification type determined based on at least one or more express indications determined automatically prior to sending the at least one electronic message to the recipient, wherein determining the at least one classification type comprises scanning the at least one electronic message by a plurality of scanners associated with the scanner, each scanner configured to scan the at least one electronic message for at least one respective classification type of a plurality of classification types;
providing, by the scanner to a machine learning trainer, the at least one electronic message and at least one identification of the at least one classification type that applies to the at least one electronic message, the machine learning trainer adapted to determine a machine learning policy that associates attributes of the at least one electronic message with the at least one classification type;
receiving, by an enforcer, a second electronic message, the second electronic message comprising one or more of the attributes that the machine learning policy associates with the at least one classification type; and
enforcing, by the enforcer, handling the second electronic message in the manner that complies with compliance criteria associated with the at least one classification type, the enforcing based on the machine learning policy indicating that the at least one classification type applies to the second electronic message.