Online Constraint Optimization for Telecommunications Resource Allocation
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Solution Overview
Problem
Telecommunications service providers face inefficiencies in customer service due to the need for approval processes for resource allocation, which can slow down interactive sessions and fail to optimize network utilization based on current trends and customer resource usage.
Innovation Solution
An online constraint optimizing service that uses historical network data and machine learning algorithms combined with business rules to determine optimal resource allocation in real-time, predicting responses to customer requests and ensuring network resources are allocated efficiently without leaving unacceptable margins for subsequent requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-approval thresholds and criteria are established for resource allocation, then customer service representative efficiency is improved, but the system cannot adapt to current network trends and optimize resource utilization dynamically
Solution Approach 1:
The patent implements dynamic threshold adjustment by continuously monitoring network utilization metrics and automatically updating approval thresholds based on current conditions. The system transitions from static pre-defined thresholds to dynamic thresholds that adapt in real-time to network trends, resolving the contradiction between efficiency and adaptability.
Solution Approach 2:
The system incorporates feedback loops where network utilization data is continuously collected, analyzed, and used to adjust approval thresholds. This feedback mechanism enables the system to learn from past decisions and optimize resource allocation dynamically, maintaining both efficiency and adaptability.
2Productivity
If real-time constraint optimization is implemented using machine learning algorithms, then network resource utilization is optimized, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary optimization service that acts as a mediator between network resources and customer requests. This service layer handles the complex machine learning algorithms and constraint optimization, isolating the complexity from the core network infrastructure and making the system more manageable while achieving optimized resource utilization.
Solution Approach 2:
The system implements self-service through automated machine learning models that independently analyze network conditions, predict resource requirements, and make allocation decisions without extensive human intervention. This automation reduces operational complexity while maintaining high resource utilization efficiency.
Data Source
AI summary
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.