US 12,293,314 B2
Systems and methods for network resource allocations
Shane Yates, Atlanta, GA (US); Joseph Keller, Atanta, GA (US); Christian Welin, Atlanta, GA (US); Alexis Hwang, Atlanta, GA (US); Jon Rambo, Atlanta, GA (US); and Brian Stublen, Atlanta, GA (US)
Assigned to Cox Communications, Inc., Atlanta, GA (US)
Filed by Cox Communications, Inc., Atlanta, GA (US)
Filed on Sep. 18, 2023, as Appl. No. 18/469,282.
Application 18/469,282 is a continuation of application No. 17/083,071, filed on Oct. 28, 2020, granted, now 11,816,610.
Prior Publication US 2024/0005234 A1, Jan. 4, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 10/00 (2023.01); G06N 5/00 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06Q 10/0631 (2023.01); G06Q 10/0639 (2023.01)
CPC G06Q 10/063116 (2013.01) [G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/06393 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
determining, by at least one processor of a cable modem termination system (CMTS) device, based on performance data associated with a plurality of customer premise devices and a plurality of nodes, a first probability of a first node experiencing a first future outage event, a second probability of a second node experiencing a second future outage event, a first optimal amount of technician resources for a first geographic area, and a second optimal amount of technician resources for a second geographic area input to a machine learning model trained using node performance data and technician resource data to predict demand for technician resources, a first score associated with the first node, the first score indicative of a first predicted demand for technician resources;
determining, based on the performance data, the first probability, the second probability, the first optimal amount of technician resources, and the second optimal amount of technician resources input to the machine learning model, a second score associated with the second node, the second score indicative of a second predicted demand for technician resources;
re-training, based on feedback based on comparisons of the first predicted demand and the second predicted demand to actual demand for technician resources at the first node and the second node, the machine learning model to generate a third score indicative of a third predicted demand for technician resources at the first node and a fourth score indicative of a fourth predicted demand for technician resources at the second node; and
causing, based on the third score and the fourth score, a third amount of technician resources to be allocated to the first geographic area and a fourth amount of technician resources to be allocated to the second geographic area.