US 10,893,064 B2
Identifying service issues by analyzing anomalies
Vinod Mukundan Menon, Bothell, WA (US); Rahul Nigam, Bothell, WA (US); Mark Gilbert, Issaquah, WA (US); and Srigopal Chitrapu, Bellevue, WA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed on Apr. 24, 2019, as Appl. No. 16/393,343.
Prior Publication US 2020/0344252 A1, Oct. 29, 2020
Int. Cl. G06F 16/906 (2019.01); G06N 20/00 (2019.01); H04L 29/06 (2006.01); G06F 16/2457 (2019.01)
CPC H04L 63/1425 (2013.01) [G06F 16/24578 (2019.01); G06F 16/906 (2019.01); G06N 20/00 (2019.01)] 19 Claims
OG exemplary drawing
 
1. A data processing system comprising:
a processor; and
a memory in communication with the processor, the memory storing instructions that when executed by the processor, cause the processor to perform functions of:
collecting data from a computing environment via a network, the data including telemetry data and change event data;
inputting the telemetry data into a first machine-learning (ML) model to identify a plurality of anomalies in the computing environment based at least in part on the telemetry data;
obtaining the identified plurality of anomalies as an output from the first ML model;
grouping the plurality of anomalies into one or more clusters;
classifying each of the one or more clusters based on a plurality of dimensions, the plurality of dimensions being determined by a second ML model,
assigning a weight to each dimension of the plurality of dimensions for each of the one or more clusters;
aggregating the weights assigned to each dimension to calculate a score for each of the one or more clusters;
generating a ranking for each of the one or more clusters based in part on the calculated score;
based on the ranking, identifying at Last one of the one or more clusters as an error requiring attention; and
transmitting data relating to the at least one of the one or more clusters for notifying a user,
wherein the plurality of dimensions includes a dimension for change events.