US 12,348,793 B2
Product metrics monitoring and anomaly detection using machine learning models
Suvrath V. Penmetcha, Mountain View, CA (US); Le Zhang, San Jose, CA (US); Vijay Anand Raghavan, Newton, MA (US); Beth Teresa Logan, Cambridge, MA (US); Kevin Henely, San Francisco, CA (US); Sahib Bal, San Jose, CA (US); and Sayan Maity, Sunnyvale, CA (US)
Assigned to Roku, Inc., San Jose, CA (US)
Filed by Roku, Inc., San Jose, CA (US)
Filed on Feb. 28, 2022, as Appl. No. 17/652,875.
Prior Publication US 2023/0276080 A1, Aug. 31, 2023
Int. Cl. H04N 21/226 (2011.01); G06F 18/21 (2023.01); G06N 5/01 (2023.01)
CPC H04N 21/226 (2013.01) [G06F 18/217 (2023.01); G06N 5/01 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
determining a combination of a plurality of values of a plurality of attributes represented by reference data associated with a plurality of computing devices by training a machine learning model based on the reference data, wherein the reference data contains an association between (i) respective values of the plurality of attributes and (ii) the plurality of computing devices entering a first device state of a plurality of possible device states, and wherein the combination of the plurality of values is correlated with entry into the first device state;
selecting at least a first computing device subset from the plurality of computing devices such that each respective computing device of the first computing device subset is associated with the combination of the plurality of values;
determining, based on a first reference data subset of the reference data, at least a first measure indicative of a first rate at which computing devices of the first computing device subset have entered the first device state during a first time period represented by the first reference data subset, wherein the first reference data subset corresponds to the first computing device subset;
determining, based on production data corresponding to one or more computing devices associated with the combination of the plurality of values, at least a second measure indicative of a second rate at which the one or more computing devices have entered the first device state during a second time period represented by the production data; and
generating, based on a comparison of the first measure to the second measure, an indication that the second rate differs from the first rate by more than a predefined threshold amount.