US 12,436,657 B1
System and method for generating a visual representation of an execution sequence within a graphical user interface
Blake Browder, Dallas, TX (US); and Joy Figarsky, Little Rock, AR (US)
Assigned to Signet Health Corporation, North Richland Hills, TX (US)
Filed by Signet Health Corporation, North Richland Hills, TX (US)
Filed on Apr. 22, 2025, as Appl. No. 19/186,298.
Application 19/186,298 is a continuation of application No. 18/957,784, filed on Nov. 24, 2024, granted, now 12,307,065.
Int. Cl. G16H 10/60 (2018.01); G06F 3/0481 (2022.01); G06F 3/0486 (2013.01)
CPC G06F 3/0481 (2013.01) [G16H 10/60 (2018.01); G06F 3/0486 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for generating a visual representation of an execution sequence within a graphical user interface, wherein the system comprises:
at least a computing device, wherein the computing device comprises: a memory; and
at least a processor communicatively connected to the memory, wherein the memory contains instructions configuring the at least a processor to: generate a display data structure, wherein generating the display data structure further comprises linking at least a visual element to at least a part of a set of sequence data, wherein linking comprises:
tokenizing a natural language input received for the at least a visual element into a plurality of components using a natural language processing model;
identifying semantic data from the plurality of components using the natural language processing model; and
linking the at least a visual element to at least a part of the set of sequence data as a function of the semantic data using the natural language processing model;
update the display data structure using the at least a visual element and at least an event handler; and
configure, using the display data structure, a display device to display the display data structure within a graphical user interface, wherein linking the at least a visual element comprises: determining a status of the at least a part of the set of sequence data as a function of a patient's adherence.