US 12,248,879 B2
Systems providing a learning controller utilizing indexed memory and methods thereto
Omar Florez Choque, Oakland, CA (US); and Erik Mueller, Chevy Chase, MD (US)
Assigned to CAPITAL ONE SERVICES, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Nov. 23, 2020, as Appl. No. 17/102,264.
Application 17/102,264 is a continuation of application No. 16/250,272, filed on Jan. 17, 2019, granted, now 10,846,594.
Prior Publication US 2021/0150368 A1, May 20, 2021
Int. Cl. G06N 20/00 (2019.01); G06N 3/084 (2023.01); H04L 9/06 (2006.01)
CPC G06N 3/084 (2013.01) [G06N 20/00 (2019.01); H04L 9/0643 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
a processor; and
memory having stored thereon:
a hash key vector and a label vector associated with the hash key vector; and
instructions that, when executed by the processor, cause the system to:
convert a current state of the memory into a memory state vector;
identify one or more saved keys of the hash key vector that are within a predetermined similarity to a candidate key, the candidate key being based at least in part on an input data vector;
implement a hash structure configured to produce collisions and comprising less of the one or more saved keys than observances, the hash structure being stored in an indexed memory;
perform, via a machine learning model, an entropy reduction estimation to determine a first estimated entropy associated with reading the at least one of the one or more identified saved keys, a second estimated entropy associated with updating the at least one of the one or more identified saved keys to include data indicative of the candidate key, and a third estimated entropy associated with inserting a new key indicative of the candidate key; and
dynamically train the machine learning model to incrementally and continuously learn new classes without retraining the machine learning model by using a trainable controller configured to learn from the indexed memory by executing, based at least in part on the entropy reduction estimation and based on the collisions from the hash structure to predict indices for new classes in a set of available entries of the hash structure, one of reading the at least one of the one or more identified saved keys, updating the at least one of the one or more identified saved keys in the memory to include data indicative of the candidate key, and inserting in the memory a new key indicative of the candidate key.