US 12,147,498 B1
Systems and methods for data grafting to enhance model robustness
Ashutosh Verma, Bangalore (IN); Tyler Case, Phoenixville, PA (US); Paul Davis, Irving, TX (US); Matt Hord, Stanley, NC (US); Ananth Kendapadi, Charlotte, NC (US); Rameshchandra Bhaskar Ketharaju, Telangana (IN); Vinothkumar Venkataraman, Bangalore (IN); Yang Angelina Yang, Mountain View, CA (US); and Naveen Gururaja Yeri, Bangalore (IN)
Assigned to Wells Fargo Bank, N.A., San Francisco, CA (US)
Filed by Wells Fargo Bank, N.A., San Francisco, CA (US)
Filed on Nov. 9, 2022, as Appl. No. 18/054,040.
Application 18/054,040 is a continuation of application No. 17/645,735, filed on Dec. 22, 2021.
Int. Cl. G06F 18/214 (2023.01); G06F 18/20 (2023.01); G06F 18/21 (2023.01)
CPC G06F 18/2148 (2023.01) [G06F 18/217 (2023.01); G06F 18/285 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A method for generating a context vector to be utilized as training data for retraining a model to mitigate deterioration of performance of the model, the method comprising:
selecting, by context vector generation circuitry, a plurality of variables defining an exogenous context for a target data point, wherein selecting the plurality of variables comprises:
determining a set of variables available corresponding to at least one of temporal or location data associated with the target data point,
determining a subset of variables from the set of variables that relate to the exogenous context based on a type of the target data point, and
selecting the subset of variables as the plurality of variables;
identifying, by the context vector generation circuitry, values for the plurality of variables based at least on a context indicator of the target data point;
generating, by the context vector generation circuitry, the context vector based on the identified values for the plurality of variables;
storing, by the context vector generation circuitry, the context vector in a known training data index;
identifying, by data grafting circuitry, the context vector as relevant to a target context vector associated with a current data point being processed by the model; and
utilizing, by model training circuitry, the context vector in connection with retraining the model.