US 12,002,545 B2
Technique for identifying features
Steven Elliot Stupp, San Carlos, CA (US)
Assigned to Exsano, Inc., Foster City, CA (US)
Filed by ExSano, Inc., Foster City, CA (US)
Filed on Feb. 17, 2018, as Appl. No. 15/898,543.
Application 15/898,543 is a continuation in part of application No. 13/507,888, filed on Aug. 2, 2012, granted, now 9,898,687.
Claims priority of provisional application 61/574,555, filed on Aug. 3, 2011.
Prior Publication US 2018/0181704 A1, Jun. 28, 2018
Int. Cl. G16B 20/20 (2019.01); G16B 20/00 (2019.01); G16B 40/00 (2019.01); G16B 40/20 (2019.01); G16B 40/30 (2019.01)
CPC G16B 20/20 (2019.02) [G16B 20/00 (2019.02); G16B 40/00 (2019.02); G16B 40/20 (2019.02); G16B 40/30 (2019.02)] 20 Claims
 
1. An electronic device, comprising:
one or more processing circuits;
memory configured to store program instructions, wherein, when executed by the one or more processing circuits, the program instructions cause the electronic device to perform one or more operations comprising:
calculating combinations of feature vectors and noise vectors, wherein a given combination in the combinations corresponds to a given feature vector in the feature vectors and a given noise vector in the noise vectors, wherein a number of the feature vectors exceeds one hundred and a number of the combinations exceeds ten thousand, and wherein the number of feature vectors is at least an order of magnitude larger than a number of entries in the given feature vector;
determining statistical associations between types of events and the combinations, wherein a given statistical association in the statistical associations corresponds to the types of events and the given combination;
computing a noise threshold associated with a group of combinations in the combinations, wherein the noise threshold corresponds to a first aggregate property of the group of combinations, wherein the first aggregate property comprises numbers of occurrences of the feature vectors in the group of combinations, and wherein the group of combinations have statistical associations equal to or greater than the noise threshold;
selecting a subset of the feature vectors based at least in part on the first aggregate property of the group of combinations; and
training a supervised machine-learning model using the types of events and a training dataset that is based at least in part on the selected subset of the feature vectors, wherein training the supervised machine-learning model comprises determining parameters in the supervised machine-learning model and identifying feature vectors in the subset of feature vectors to use in the supervised machine-learning model, and wherein the supervised machine-learning model predicts the types of events based at least in part on the identified feature vectors and the parameters.