US 11,670,185 B2
Adaptive machine learning system
Rajiv Baphna, Bangalore (IN); Satyamoy Chatterjee, Bangalore (IN); Halasya Siva Subramania, Bangalore (IN); and Ashutosh Joshi, Bangalore (IN)
Assigned to Analyttica Datalab Inc., Wilmington, DE (US)
Filed by Analyttica Datalab Inc., Wilmington, DE (US)
Filed on Oct. 14, 2020, as Appl. No. 17/70,715.
Application 17/070,715 is a continuation of application No. 15/912,333, filed on Mar. 5, 2018, abandoned.
Application 15/912,333 is a continuation in part of application No. 15/888,799, filed on Feb. 5, 2018, abandoned.
Application 15/888,799 is a continuation of application No. 14/477,843, filed on Sep. 4, 2014, granted, now 9,886,867, issued on Feb. 6, 2018.
Claims priority of application No. 3975/CHE/2013 (IN), filed on Sep. 5, 2013.
Prior Publication US 2021/0027647 A1, Jan. 28, 2021
Int. Cl. G09B 7/00 (2006.01); G09B 5/00 (2006.01); G09B 5/08 (2006.01); G09B 7/04 (2006.01)
CPC G09B 7/00 (2013.01) [G09B 5/00 (2013.01); G09B 5/08 (2013.01); G09B 7/04 (2013.01)] 19 Claims
OG exemplary drawing
 
1. An apparatus comprising:
a processor; and
a memory that stores code executable by the processor to:
continuously monitor one or more interactions of a user with a computing device while the user performs one or more simulated tasks digitally presented to the user via the computing device, the one or more simulated tasks associated with a learning path;
track data describing the user's interactions during the user's performance of the one or more simulated tasks of the learning path;
dynamically and in real-time, optimize the user's learning path by simulating multiple different learning paths using one or more machine learning processes and the tracked data;
present one or more recommendations to the user for optimizing the user's learning path, the one or more recommendations generated as a function of the optimized learning path, the one or more recommendations comprising one or more hints that are presented to the user via the computing device at predetermined time intervals based on steps taken during the user's learning path; and
convert the user's interactions for performing the one or more simulated tasks into code for one or more programming languages.