US 12,271,920 B2
Systems and methods for features engineering
Sebastien Ouellet, Ottawa (CA); Zhen Lin, Ottawa (CA); Christopher Wang, Ottawa (CA); and Chantal Bisson-Krol, Ottawa (CA)
Assigned to Kinaxis Inc., Ottawa (CA)
Filed by Kinaxis Inc., Ottawa (CA)
Filed on Nov. 24, 2022, as Appl. No. 17/993,952.
Application 17/993,952 is a continuation of application No. 16/837,182, filed on Apr. 1, 2020, granted, now 11,537,825.
Application 16/837,182 is a continuation in part of application No. 16/599,143, filed on Oct. 11, 2019, granted, now 11,526,899.
Prior Publication US 2023/0085701 A1, Mar. 23, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 10/06 (2023.01); G06F 18/20 (2023.01); G06F 18/21 (2023.01); G06F 18/211 (2023.01); G06F 18/22 (2023.01); G06F 18/25 (2023.01); G06N 20/00 (2019.01); G06Q 30/02 (2023.01); G06Q 30/0204 (2023.01)
CPC G06Q 30/0205 (2013.01) [G06F 18/211 (2023.01); G06F 18/217 (2023.01); G06F 18/22 (2023.01); G06F 18/251 (2023.01); G06F 18/285 (2023.01); G06N 20/00 (2019.01)] 18 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, by a processor, internal signal data;
receiving, by the processor, external signal data;
fusing, by the processor, data from the internal signal data and the external signal data, the fusing based on meta-data of each of the internal signal data and each of the external signal data;
generating, by the processor, a plurality of features based on one or more valid combinations that match a transformation input, the transformation forming part of a library of transformations;
selecting, by the processor, one or more features from the plurality of features, based on a predictive strength of each feature, to provide a set of selected features;
training and/or validating, by the processor, one or more machine learning models using the set of selected features;
retraining, by the processor, the one or more machine learning models on an expanded engineered data set comprising data corresponding to the training and/or validation portions of the data as (i) part of a model selection process, or (ii) without the model selection process;
generating, by the processor, prediction data utilizing the retrained one or more machine learning models.