Action Impact Analysis for Mobile Network ML Models
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Solution Overview
Problem
Current ML model performance monitoring in mobile networks primarily focuses on accuracy, neglecting the impact of predictions and recommendations on network performance, which can lead to suboptimal resource allocation and service level agreement violations due to lack of action impact analysis.
Innovation Solution
Implementing an apparatus and method for action impact analysis that collects and evaluates the impact of actions taken based on ML model analytics, predictions, or recommendations, allowing for feedback and potential model updates or changes, including data collection, monitoring, and reporting mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ML model accuracy monitoring is implemented, then model prediction quality is improved, but comprehensive evaluation of action impact on network performance deteriorates due to lack of action impact analysis
Solution Approach 1:
The patent implements a feedback mechanism where the analytics consuming entity receives action impact analysis results from the analytics producing entity. The system collects actual network performance data after actions are enforced, compares it with predicted outcomes, and feeds this information back to evaluate ML model performance. This closed-loop feedback enables comprehensive evaluation of both prediction accuracy and actual action impact on network performance.
2Reliability
If action impact analysis is implemented, then network performance evaluation is improved, but system complexity deteriorates due to additional data collection and monitoring requirements
Solution Approach 1:
The patent makes the analytics producing entity multi-functional by enabling it to not only generate ML model analytics and predictions but also to collect action impact data, monitor network performance, and generate comprehensive evaluation reports. The analytics consuming entity similarly performs multiple functions including enforcing actions, collecting performance data, and evaluating results. This multi-functionality reduces the need for separate dedicated systems for each function.
Solution Approach 2:
The system implements self-service mechanisms where the analytics producing entity automatically collects the necessary data for action impact analysis, processes the performance information, and generates evaluation reports without requiring external intervention. The analytics consuming entity autonomously enforces actions and collects performance data, reducing operational complexity and manual involvement.
3Measurement precision
If comprehensive data collection and monitoring is performed, then action impact evaluation accuracy is improved, but data processing time and resources deteriorate
Solution Approach 1:
The patent extracts and monitors only the specific network performance parameters and indicators that are directly relevant to evaluating action impact, rather than collecting all possible data. The system identifies key performance indicators (KPIs) that matter for the specific ML model actions being evaluated, extracting only the necessary data points needed for accurate evaluation while minimizing unnecessary data collection and processing overhead.
Data Source
AI summary
An apparatus configured to: transmit, to an analytics producing entity, an indication to perform action impact analysis for a machine learning model; receive, from the analytics producing entity, a message comprising at least one of: analytics, a prediction, or a recommendation from the machine learning model; select at least one action based, at least partially, on the received message; enforce the at least one selected action; obtain an action impact analysis for the at least one enforced action and the machine learning model; evaluate the action impact analysis; and determine whether to request a change regarding the machine learning model in response to the evaluation of the action impact analysis.