US 12,217,627 B1
Apparatus and method for determining action guides
Barbara Sue Smith, Toronto (CA); and Daniel J. Sullivan, Toronto (CA)
Assigned to The Strategic Coach Inc., Toronto (CA)
Filed by The Strategic Coach Inc., Toronto (CA)
Filed on Dec. 28, 2023, as Appl. No. 18/398,446.
Int. Cl. G06Q 10/0639 (2023.01); G06Q 10/0633 (2023.01); G09B 5/02 (2006.01); G06N 20/00 (2019.01)
CPC G09B 5/02 (2013.01) [G06Q 10/0633 (2013.01); G06Q 10/0639 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus for determining action guides, wherein the apparatus comprises:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive a user action, wherein the user action comprises a user usage;
convert the user usage of the user action into a current usage;
obtain action template data, wherein the action template data comprises a template usage configured to determine a relevancy score corresponding to a relevancy strength of data from a web crawler function;
generate training data, wherein the training data comprises a template action expectation as a function of the user action and the action template data;
determine an action feasibility as a function of the current usage and the template action expectation, wherein determining the action feasibility comprises: iteratively training a machine learning model as a function of the training data, wherein iteratively training the machine learning model further comprises:
using the training data applied to an input layer of nodes comprising a plurality of data entries of user action and action template data inputs, one or more intermediate layers of nodes, and an output layer of nodes comprising a plurality of template action expectation, action feasibility, and action guide outputs;
updating the outputs based on an error function iteratively, wherein an error function value is evaluated in training iterations and compared to a threshold;
comparing an output generated by the machine learning model to an input in the training data;
adjusting one or more connections between nodes in adjacent layers of the machine learning model as a function of weighted sums of the inputs;
detecting a scoring function between the output layer of nodes and the input layer of nodes;
determining a correlation between the output layer of nodes and the input layer of nodes;
updating the training data as a function of the scoring function; and
retraining the machine learning model as a function of the scoring function; and
generate an action guide as a function of the action feasibility.