US 12,260,308 B2
Apparatus for post action planning and method of use
Barbara Sue Smith, Toronto (CA); and Daniel J. Sullivan, Toronto (CA)
Assigned to The Strategic Coach Inc., Toronto (CA)
Filed by Strategic Coach, Toronto (CA)
Filed on May 2, 2023, as Appl. No. 18/142,411.
Prior Publication US 2024/0370763 A1, Nov. 7, 2024
Int. Cl. G06N 20/00 (2019.01); G16H 20/30 (2018.01); G16H 50/30 (2018.01)
CPC G06N 20/00 (2019.01) [G16H 20/30 (2018.01); G16H 50/30 (2018.01)] 19 Claims
OG exemplary drawing
 
1. An apparatus for post action planning, the apparatus comprising:
at least a processor;
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive user experience data from an input device and a web crawler, wherein the input device comprises an audiovisual input device, wherein the user experience data is further derived from the web crawler, wherein the web crawler is configured to detect at least one data pattern;
identify at least a learning datum as a function of the user experience datum, wherein identifying the at least a learning datum comprises identifying one or more outcomes associated with the user experience data;
generate growth data as a function of the user experience data, wherein the growth data includes a growth score generated based on a comparison between the growth data and an optimal output datum describing an optimal response associated with an action from the user experience data, wherein the growth score determines a degree of similarity between the growth data and the optimal output datum;
determine at least one post action plan as a function of the at least a learning datum and the growth data comprising:
receiving post action training data comprising a plurality of growth data and a plurality of learning data correlated to a plurality of post action plans;
training a post action machine learning model as a function of the post action training data, wherein training the post action machine learning model comprises:
iteratively updating the post action training data as a function of input and output results of the post action machine learning model; and
retraining the post action machine learning model with an updated post action training data; and
generating the at least one post action plan as a function of the post action machine learning model;
create a user interface data structure, wherein the user interface data structure comprises the at least one post action plan; and
transmit the at least one post action plan and the user interface data structure.