US 12,105,772 B2
Dynamic and continuous composition of features extraction and learning operation tool for episodic industrial process
Shrey Shrivastava, White Plains, NY (US); Dhavalkumar C. Patel, White Plains, NY (US); Jayant R. Kalagnanam, Briarcliff Manor, NY (US); and Chandrasekhara K. Reddy, Kinnelon, NJ (US)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Dec. 1, 2020, as Appl. No. 17/109,022.
Prior Publication US 2022/0172002 A1, Jun. 2, 2022
Int. Cl. G06K 9/62 (2022.01); G06F 9/54 (2006.01); G06F 18/2113 (2023.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01)
CPC G06F 18/2155 (2023.01) [G06F 9/541 (2013.01); G06F 18/2113 (2023.01); G06N 20/00 (2019.01)] 14 Claims
OG exemplary drawing
 
1. A computer implemented method of preparing process data for use in an artificial intelligence (AI) model, the method executed by one or more processors of a computer, the method comprising:
collecting and storing raw data as episodic data for each episode of a process;
assigning an episode identifier each set of episodic data;
transforming the raw data per episode into a format of standardized episodic data usable by the AI model;
assigning metrics to the standardized episodic data;
aggregating the standardized episodic data in an episode store;
improving a processor cycle efficiency and decreasing storage requirements of the computer by:
processing the standardized episodic data related to on arrival features as the data is received;
processing the standardized episodic data related to borderline features when computation resource usage permits; and
processing the standardized episodic data related to on demand features when needed by the AI model, and
optimizing feature generation tasks when an application programming interface (API) call of the AI model is invoked.