US 12,321,824 B1
Pipelined machine learning frameworks
Matthew Reeves, Boston, MA (US); and Ben Thompson, Bellevue, WA (US)
Assigned to Liberty Mutual Insurance Company, Boston, MA (US)
Filed by Liberty Mutual Insurance Company, Boston, MA (US)
Filed on Jan. 7, 2021, as Appl. No. 17/143,769.
Claims priority of provisional application 62/958,379, filed on Jan. 8, 2020.
Int. Cl. G06F 18/214 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01)
CPC G06N 20/00 (2019.01) [G06F 18/214 (2023.01); G06N 5/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for generating a predictive output based at least in part on an input data object, the computer-implemented method comprising:
generating, by one or more processors, one or more inference error correction engineered features based at least in part on the input data object, wherein the one or more inference error correction engineered features include an agent-based error likelihood value describing an estimated error likelihood of an input provider agent associated with the input data object;
processing, by the one or more processors, the one or more inference error correction engineered features, using a trained error correction machine learning model of a pipelined machine learning framework, to generate an error-corrected input data object in which one or more input data fields corresponding to the input data object are adjusted based at least in part on the one or more inference error correction engineered features; and
generating, by the one or more processors, the predictive output based at least in part on the error-corrected input data object.