US 12,217,145 B2
Continuously learning, stable and robust online machine learning system
Tanju Cataltepe, Istanbul (TR)
Assigned to Tazi AI Systems, Inc., San Francisco, CA (US)
Filed by Tazi AI Systems, Inc., Sausalito, CA (US)
Filed on Mar. 15, 2022, as Appl. No. 17/695,787.
Application 17/695,787 is a continuation of application No. 16/125,742, filed on Sep. 9, 2018, granted, now 11,315,030.
Claims priority of provisional application 62/639,490, filed on Mar. 6, 2018.
Prior Publication US 2022/0207399 A1, Jun. 30, 2022
Int. Cl. G06N 3/04 (2023.01); G05B 13/02 (2006.01); G05B 23/02 (2006.01); G06F 3/16 (2006.01); G06F 16/2455 (2019.01); G06F 18/10 (2023.01); G06F 18/15 (2023.01); G06F 18/21 (2023.01); G06F 18/2115 (2023.01); G06F 18/23 (2023.01); G06F 18/40 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 5/04 (2023.01); G06N 5/043 (2023.01); G06N 5/045 (2023.01); G06N 7/00 (2023.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06V 10/28 (2022.01); G06V 10/70 (2022.01); G06V 10/72 (2022.01); G06V 10/77 (2022.01); G06V 10/778 (2022.01); G06V 10/80 (2022.01)
CPC G06N 20/20 (2019.01) [G05B 13/028 (2013.01); G05B 23/0221 (2013.01); G05B 23/0229 (2013.01); G06F 3/165 (2013.01); G06F 16/24568 (2019.01); G06F 18/10 (2023.01); G06F 18/15 (2023.01); G06F 18/2115 (2023.01); G06F 18/2178 (2023.01); G06F 18/23 (2023.01); G06F 18/40 (2023.01); G06N 3/04 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 5/043 (2013.01); G06N 5/045 (2013.01); G06N 7/00 (2013.01); G06N 20/00 (2019.01); G06V 10/28 (2022.01); G06V 10/70 (2022.01); G06V 10/72 (2022.01); G06V 10/77 (2022.01); G06V 10/778 (2022.01); G06V 10/7784 (2022.01); G06V 10/80 (2022.01); G06V 10/803 (2022.01); G06T 2207/20081 (2013.01)] 45 Claims
OG exemplary drawing
 
1. An Online Machine Learning System (OMLS) implemented on one or more electronic devices comprising one or more processors, the system comprising:
an Online Preprocessing Engine (OPrE) stored on a non-transitory computer readable memory and implemented using the one or more processors and configured to (a) receive streaming data including a set of instances, each instance of the set of instances comprising one or more vectors of inputs that include a plurality of continuous or categorical features produced externally or internally by the OMLS or by actions of one or more of users of the OMLS; (b) discretize features; (c) impute missing feature values; (d) normalize features; (e) detect drift or change in features; and (f) detect drift or change in labels for the set of instances, wherein each of the labels is an actual label or a predicted label;
an Online Feature Engineering Engine (OFEE) stored on a non-transitory computer readable memory and implemented using the one or more processors and configured to produce features including engineered features;
an Online Robust Feature Selection Engine (ORFSE) stored on a non-transitory computer readable memory and implemented using the one or more processors, and configured to evaluate and select features by computing a relevance value of one of the features based on a computation of a degree of correlation between a first feature and a second feature using at least one of (i) mutual information-based label relevances between features, (ii) distributional similarities between features, or (iii) similarity of change in distributions of two features to reduce correlations and redundancies between selected features and performing a statistical significance test to determine a significance of the relevance value that is computed using at least one of (i), (ii), or (iii); and
an Online Machine Learning Engine (OMLE) stored on a non-transitory computer readable memory and implemented using the one or more processors, and configured to incorporate and utilize one or more machine learning algorithms or models utilizing features selected by the ORFSE, to generate a result, and capable of incorporating and utilizing multiple different machine learning algorithms or models,
wherein each of the OMLE, the OPrE, the OFEE, and the ORFSE are continuously communicatively coupled to each other,
wherein the OPrE, which is capable of detecting the drift or change in features, is capable of detecting the drift or changes in the plurality of continuous or categorical features, the discretized features, and the engineered features, and
wherein the OMLS is configured to perform continuous online machine learning.