US 12,406,173 B2
Covariate processing with neural network execution blocks
Daniel Wong, Montreal (CA); Dmitri Carpov, Montreal (CA); and Nicolas Chapados, Montreal (CA)
Assigned to ServiceNow, Inc., Santa Clara, CA (US)
Filed by ServiceNow, Inc., Santa Clara, CA (US)
Filed on Oct. 30, 2020, as Appl. No. 17/085,555.
Prior Publication US 2022/0138539 A1, May 5, 2022
Int. Cl. G06N 3/06 (2006.01); G06N 3/049 (2023.01); G06N 3/08 (2023.01)
CPC G06N 3/06 (2013.01) [G06N 3/049 (2013.01); G06N 3/08 (2013.01)] 24 Claims
OG exemplary drawing
 
1. A method for forecasting future values of a target variable using past values thereof, values of the target variable being affected by one or more covariates wherein the one or more covariates are independent from the target variable, the method comprises:
using a covariate-specific AI model, computing a covariate effect of the one or more covariates on the target variable, the covariate effect being a defined modification to the values of the target variable caused by the one or more covariates;
computing intrinsic past values of the target variable by removing the covariate effect of the one or more covariates from past values of the target variable;
using a target-variable-specific AI model, generating an intrinsic forecast of the future values of the target variable; and
computing a forecast that includes the covariate effect using the intrinsic forecast of the future values of the target variable and the covariate effect of the one or more covariates;
wherein the covariate-specific AI model and the target-variable-specific AI model are pre-trained.