US 12,445,347 B2
Identification of root causes in data processing errors
Robert Kierzyk, Littleton, CO (US)
Assigned to DISH Wireless L.L.C., Englewood, CO (US)
Filed by DISH Wireless L.L.C., Englewood, CO (US)
Filed on Dec. 30, 2022, as Appl. No. 18/148,978.
Claims priority of provisional application 63/295,799, filed on Dec. 31, 2021.
Prior Publication US 2023/0216727 A1, Jul. 6, 2023
Int. Cl. H04L 41/0631 (2022.01); H04L 41/16 (2022.01)
CPC H04L 41/0631 (2013.01) [H04L 41/16 (2013.01)] 14 Claims
OG exemplary drawing
 
1. An automated process to identify root causes of defects in data processing results emanating from a data processing system, wherein the automated process comprises:
receiving the data processing results from a plurality of different data processing components of a cloud-based 5g wireless communications network, wherein each of the different data processing components of the cloud-based 5g wireless communications network executes as a virtual component of the data processing system wherein the data processing results comprise a then-current system load and a number of virtual distributed unit (DU) modules currently in operation by the cloud-based 5g wireless communications network;
identifying a defect in the data processing results received from one or more of the virtual components of the data processing system, wherein the defect is recognized when the number of virtual DU modules changes unexpectedly given a then-current system load of the cloud-based 5g wireless communications network;
storing defect data about the identified defect in a database, the defect data identifying the defect and comprising additional information associated with the defect, wherein the additional information identifies at least one of the one or more virtual components of the data processing that generated the defect and comprises technical conditions of the cloud-based 5g wireless communications network at the time of the defect;
detecting a pattern in the defect data based upon commonalities in the conditions of the cloud-based 5g wireless communications network that are associated with multiple defects; and
predicting additional defects relating to the one or more virtual components of the cloud-based 5g wireless network based upon the detected pattern in the defect data; and
updating test vectors based upon the detected pattern and applying the updated test vectors to the cloud-based 5G wireless communications network.