公开(公告)号：US11954177B2

公开(公告)日：2024-04-09

申请号：US17101258

申请日：2020-11-23

Applicant: Verizon Patent and Licensing Inc.

Inventor： Kushal Singla ,  Sidakpreet Singh Chawla ,  Ebenezer Bardhan ,  Pooja Khivesara

IPC: H04L12/725 ,  G06F18/20 ,  G06F18/2431

CPC classification number: G06F18/2431 ,  G06F18/285

Abstract: Systems and methods described herein provide an online constraint optimizing service that evaluates user requests and inquiries for telecommunications services in the context of a real-time constraint-based analysis. According to an implementation, a network device receives a function for an analytic event and a constraint. The function applies different user attributes for a telecommunications network. The network device generates a training data set using offline constrained optimization of the function. The network device develops a predictive model for utilization of network resources in the telecommunications network using the training data set. The network device receives a user request that corresponds to the analytic event addressed by the predictive model and conducts an online prescriptive analysis using the predictive model. The network device optimizes allocation of the network resources to the user based on the prescriptive analysis. The network device monitors the model recommendations and adapts the predictive model for concept drift while maintaining the constraint.

公开(公告)号：US20220164593A1

公开(公告)日：2022-05-26

申请号：US17101258

申请日：2020-11-23

Applicant: Verizon Patent and Licensing Inc.

Inventor： Kushal Singla ,  Sidakpreet Singh Chawla ,  Ebenezer Bardhan ,  Pooja Khivesara

IPC: G06K9/62

Abstract: Systems and methods described herein provide an online constraint optimizing service that evaluates user requests and inquiries for telecommunications services in the context of a real-time constraint-based analysis. According to an implementation, a network device receives a function for an analytic event and a constraint. The function applies different user attributes for a telecommunications network. The network device generates a training data set using offline constrained optimization of the function. The network device develops a predictive model for utilization of network resources in the telecommunications network using the training data set. The network device receives a user request that corresponds to the analytic event addressed by the predictive model and conducts an online prescriptive analysis using the predictive model. The network device optimizes allocation of the network resources to the user based on the prescriptive analysis. The network device monitors the model recommendations and adapts the predictive model for concept drift while maintaining the constraint.