US 12,293,412 B1
Generation of time-interval-specific support vector machine
Jayden Seowook Jang, New York, NY (US)
Assigned to Chicago Mercantile Exchange Inc., Chicago, IL (US)
Filed by Chicago Mercantile Exchange Inc., Chicago, IL (US)
Filed on Jun. 22, 2022, as Appl. No. 17/846,811.
Int. Cl. G06Q 40/04 (2012.01); G06F 18/2411 (2023.01)
CPC G06Q 40/04 (2013.01) [G06F 18/2411 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, over an electronic communications network and from a plurality of participant network nodes, a plurality of request electronic data messages and a plurality of counter-request electronic data messages for a defined time interval;
for each of the plurality of request electronic data messages and the plurality of counter-request electronic data messages:
determining, by a processor, a corresponding imputed variability level;
generating, by the processor, an input code for the electronic data message the input code based on:
a corresponding execution value for the electronic data message, wherein the corresponding execution value includes an execution value outside a data gap range of execution values for which no corresponding request electronic data messages or counter-request electronic data messages were received within the defined time interval; and
the corresponding imputed variability level;
generating, by the processor, a time-interval-specific support vector machine for the defined time interval by applying the input codes to an initial-state support vector machine as a training input;
generating, by the processor, a dummy data set by generating a plurality of imputed variability level and execution value (IVEV) tuples;
after generating the time-interval-specific support vector machine, applying, by the processor, the dummy data set to the time-interval-specific support vector machine to obtain classified dummy data output for each of the plurality of IVEV tuples; and
for each of a plurality of execution values including gap execution values within the data gap range:
determining, by the processor and based on the classified dummy data output, a corresponding imputed variability boundary level across which classification of request-type for the execution value.