	US 12,107,809 B2
	Formatting electronic messages using machine learning
Rohit Pradeep Shetty, Bangalore (IN); Ravish Chawla, Atlanta, GA (US); and Adam Chow, Atlanta, GA (US)
Assigned to Omnissa, LLC, Palo Alto, CA (US)
Filed by VMWARE, INC., Palo Alto, CA (US)
Filed on Feb. 2, 2022, as Appl. No. 17/590,836.
Claims priority of application No. 202141055431 (IN), filed on Nov. 30, 2021.
Prior Publication US 2023/0171210 A1, Jun. 1, 2023
Int. Cl. H04L 51/066 (2022.01); G06F 40/103 (2020.01); G06F 40/284 (2020.01); G06N 20/00 (2019.01)

CPC H04L 51/066 (2013.01) [G06F 40/103 (2020.01); G06F 40/284 (2020.01); G06N 20/00 (2019.01)]

16 Claims

1. A system comprising:

at least one computing device comprising a processor and a memory; and

machine-readable instructions stored in the memory that, when executed by the processor, cause the at least one computing device to at least:

obtain an electronic message to be transmitted to a plurality of recipients including at least a first recipient and a second recipient;

generate a processed message based at least in part on the electronic message;

determine at least one attribute for the processed message;

generate a first formatting specification for reformatting text in a body of the electronic message for the first recipient based at least in part on a mapping of the at least one attribute to a formatting preference defined for the first recipient of the electronic message and a second formatting specification for reformatting text in a body of the electronic message for the second recipient based at least in part on a mapping of the at least one attribute to a formatting preference defined for the second recipient of the electronic message; and

generate a first reformatted message for the first recipient based at least in part on the first formatting specification and a second reformatted message for the second recipient based at least in part on the second formatting specification.