US 11,720,846 B2
Artificial intelligence-based use case model recommendation methods and systems
Sastry Vsm Durvasula, Phoenix, AZ (US); Rares Almasan, Phoenix, AZ (US); Neema Uthappa, Phoenix, AZ (US); Sriram Venkatesan, Princeton Junction, NJ (US); and Sayan Chowdhury, Nagpur (IN)
Assigned to MCKINSEY & COMPANY, INC., New York, NY (US)
Filed by MCKINSEY & COMPANY, INC., New York, NY (US)
Filed on Apr. 1, 2022, as Appl. No. 17/711,880.
Claims priority of application No. 202121056829 (IN), filed on Dec. 7, 2021.
Prior Publication US 2023/0177441 A1, Jun. 8, 2023
Int. Cl. G06N 5/022 (2023.01); G06Q 10/0639 (2023.01); G06Q 10/067 (2023.01); G06Q 10/0637 (2023.01)
CPC G06Q 10/06393 (2013.01) [G06N 5/022 (2013.01); G06Q 10/067 (2013.01); G06Q 10/06375 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for receiving and processing use case information, comprising:
receiving, via an electronic network, a plurality of user use case experiments;
analyzing, via one or more processors, the use case experiments using an artificial intelligence engine to order each of the plurality of use case experiments, wherein the analyzing includes scoring each of the use case experiments using a mapper-recommender module to predict a respective efficiency of each use case for performing a supervised or unsupervised machine learning technique;
generating, via one or more processors, one or more optimized machine learning models based on the ordered user use case experiments; and
causing, via one or more processors, one or more optimized deployment options to be displayed in a client computing device, each including at least one respective computing resource and at least one respective target platform.