US 12,033,216 B1
System and method for a machine learning service
Ankita Joshi, Karnataka (IN); Ashish Singh, Karnataka (IN); Manoj Arora, East Brunswick, NJ (US); Poojan Turakhia, Maharashtra (IN); Jeff Preston, New York, NY (US); and Frank Hong, New York, NY (US)
Assigned to Morgan Stanley Services Group Inc., New York, NY (US)
Filed by Morgan Stanley Services Group Inc., New York, NY (US)
Filed on May 19, 2023, as Appl. No. 18/320,752.
Int. Cl. G06Q 40/06 (2012.01)
CPC G06Q 40/06 (2013.01) 16 Claims
OG exemplary drawing
 
1. A computer-implemented method of providing recommendations for matching leads to financial advisors using a machine learning model, the method, executed by one or more hardware processing units of a computing system, comprising:
training the machine learning model using known attributes of the leads and advisors to determine optimal lead/advisor matches and to output an initial recommendation list of lead/advisor matched pairs;
serializing the machine learning model in a platform independent manner wherein structures developed in a high-level programming context are converted into lower-level bytes not specific to a particular programming platform using a graph-based portable file format bundle constituting a plurality of files, wherein the bundle includes a first file that stores meta data including a directory which contains a root transformer of the machine learning model, a second file that stores information detailing the machine learning model, and a third file that stores information concerning machine learning model features employed in a specific machine learning platform;
providing access to the serialized machine learning model to an end user device via an application program interface;
deserializing the machine learning model that is executed at the end user's device, into an end user-executable model that is agnostic with respect to any platform with which the end user device operates by recreating an architecture of the executable machine learning model from the serialized plurality of files;
receiving preferences from the end user regarding a preferred financial advisor;
executing the deserialized machine learning model at the end user device using the received preferences to determine a list of advisor recommendations tailored for the end user;
filtering the list of advisor recommendations using a first set of rules and a second set of business rules specific to a platform associated with the end user which disallow certain lead/advisor pairings;
outputting a representation of the filtered recommendations at the end user device for viewing by a particular end user; and
transmitting a selection from the filtered recommendations from the end user device for further training of the machine learning module with respect to the particular end user.