US 12,277,154 B1
Systems and methods for machine learning models to assess and extract data from user inputs
Vinod Khosla, Portola Valley, CA (US); Neeru Khosla, Portola Valley, CA (US); Miral Shah, San Jose, CA (US); and Reza Shahbazi, Hillsborough, CA (US)
Assigned to CK12 Foundation, Menlo Park, CA (US)
Filed by CK12 Foundation, Menlo Park, CA (US)
Filed on Sep. 6, 2024, as Appl. No. 18/827,005.
Claims priority of provisional application 63/581,419, filed on Sep. 8, 2023.
Int. Cl. G06F 16/334 (2025.01); G06F 40/35 (2020.01)
CPC G06F 16/3344 (2019.01) [G06F 40/35 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A non-transitory processor-readable medium storing code representing instructions to be executed by one or more processors, the instructions comprising code to cause the one or more processors to:
receive, a plurality of inputs associated with a behavior of a user;
classify, based on a first machine learning model and using at least one input rubric, each input from the plurality of inputs into an input type from a plurality of input types, the first machine learning model configured to extract relevant classification data from the plurality of inputs;
define, based on the input type of each input from the plurality of inputs, a first set of inputs associated with a first evaluation type and a second set of inputs associated with a second evaluation type, wherein the second evaluation type is associated with a lower computational cost than the first evaluation type;
select, based on the first evaluation type, a second machine learning model, the second machine learning model trained based on analytical data associated with the user;
extract, using the second machine learning model, from the first set of inputs a pattern associated with the user;
evaluate a first state of the user based on the pattern and a second state of the user based on the second set of inputs; and
generate a grade of the user based on the first state and the second state.