Granted Patent B1

US 12,314,536 · App. 18/957,744 · Granted May 27, 2025

Inventors: Blake Browder (Dallas, TX); Joy Figarsky (Little Rock, AR)

Assignee: Signet Health Corporation

G06F3/0482G06Q50/265G16H40/20

Abstract

Claims (47)

1. A system for generating a user-sensitive user interface,

wherein the system comprises:

a display device, wherein the display device is configured to display a graphical user interface;

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 execution operations as a function of a task module;

display, through the display device at a first node, the execution operations in the graphical user interface, wherein the execution operations each comprise an input event handler;

receive, through the graphical user interface and one of the input event handlers, user response data corresponding to one or more of the execution operations;

determine, as a function of the user response data, an assigned status corresponding to the one or more execution operations using a machine-learning model;

generate a second execution operation and an assigned node as a function of the assigned status;

generate an updated user interface as a function of the second execution operation and the assigned status; and

transmit the updated user interface to the assigned node, wherein the assigned node is configured to display the updated user interface.

2. The system of claim 1 , wherein generating the second execution operation comprises a rating interface element, wherein the rating interface element may be configured to receive a rating datum.

3. The system of claim 2 , wherein the user interface is updated as a function of the rating interface element.

4. The system of claim 1 , wherein determining the assigned status comprises analyzing user response data using at least a tonality machine-learning model to determine tone data.

5. The system of claim 4 , wherein the at least a tonality machine-learning model is iteratively trained based on system feedback.

6. The system of claim 4 , wherein the at least a tonality machine-learning model comprises an encoder configured to generate a plurality of textual encodings as a function of the user response data and a classifier configured to classify the plurality of textual encodings into a tone classification.

7. The system of claim 6 , wherein the encoder is trained using a self-supervised training process wherein:

an output of the encoder is input into a decoder which is configured to output estimated user response data; and

the encoder is trained as a function of the estimated user response data appended with a tone label.

8. The system of claim 1 , wherein the at least a processor is configured to display the second execution operation through the display device at the first node.

9. The system of claim 1 , wherein the user interface comprises a plurality of interactive elements for each execution operation of the one or more execution operations.

10. The system of claim 1 , wherein generating the second execution operation and the assigned node as a function of the assigned status comprises:

classifying the second execution operation to an end user pool; and

selecting an end user from the end user pool, wherein the end user has an association with the assigned node.

11. A method for generating a user-sensitive user interface, wherein the method comprises:

generating, by at least a processor, execution operations as a function of a task module;

displaying, through a display device at a first node, the execution operations in the graphical user interface, wherein the execution operations each comprise an input event handler;

receiving, through a graphical user interface and one of the input event handlers, user response data corresponding to one or more of the execution operations;

determining, by the at least a processor, as a function of the user response data, an assigned status corresponding to the one or more execution operations using a machine-learning model;

generating, by the at least a processor, a second execution operation and an assigned node as a function of the assigned status;

generating, by the at least a processor, an updated user interface as a function of the second execution operation and the assigned status; and

transmitting, by the at least a processor, the updated user interface to the assigned node, wherein the assigned node is configured to display the updated user interface.

12. The method of claim 11 , wherein generating the second execution operation comprises a rating interface element, wherein the rating interface element may be configured to receive a rating datum.

13. The method of claim 12 , wherein the user interface is updated as a function of the rating interface element.

14. The method of claim 11 , wherein determining the assigned status comprises analyzing user response data using at least a tonality machine-learning model to determine tone data.

15. The method of claim 14 , wherein the at least a tonality machine-learning model is iteratively trained based on system feedback.

16. The method of claim 14 , wherein the at least a tonality machine-learning model comprises an encoder configured to generate a plurality of textual encodings as a function of the user response data and a classifier configured to classify the plurality of textual encodings into a tone classification.

17. The method of claim 16 , wherein the encoder is trained using a self-supervised training process wherein:

an output of the encoder is input into a decoder which is configured to output estimated user response data; and

the encoder is trained as a function of the estimated user response data appended with a tone label.

18. The method of claim 11 , wherein the at least a processor is configured to display the second execution operation through the display device at the first node.

19. The method of claim 11 , wherein the user interface comprises a plurality of interactive elements for each execution operation of the one or more execution operations.

20. The method of claim 11 , wherein generating the second execution operation and the assigned node as a function of the assigned status comprises:

classifying the second execution operation to an end user pool; and

selecting an end user from the end user pool, wherein the end user has an association with the assigned node.

Assignments (3)

References Cited (11)