ML Deviation Prediction for Interconnected Metrics
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Solution Overview
Problem
Traditional methods for predicting deviations in metrics are inadequate due to reliance on static, deterministic predictions that fail to account for dynamic factors, requiring high human expertise and struggling with complex, high-volume data scenarios, leading to inaccurate decision-making.
Innovation Solution
A processor-implemented method using machine learning to predict deviations, derive interconnections between metrics, and provide action recommendations through interconnected models, leveraging external data sources and reinforcement learning for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional statistics and rules-based methods are used for deviation detection, then the system is simple to implement, but the prediction accuracy deteriorates due to inability to handle complex relationships and high volume data
Solution Approach 1:
The patent replaces traditional mechanical/statistical methods with machine learning models. Specifically, it uses ML models to analyze complex relationships between metrics and predict deviations, substituting the limited capability of rules-based systems with adaptive learning systems that can handle terabytes of data and thousands of factors.
Solution Approach 2:
The patent changes the parameters of the prediction system by transitioning from static, deterministic predictions to dynamic predictions with probability distributions. It uses ML models that can process varying inputs and outputs, adapting to changing conditions rather than relying on fixed statistical rules.
2Device complexity
If static and deterministic predictions are made, then the prediction method is simple, but the reliability deteriorates due to decaying accuracy over time in dynamic environments
Solution Approach 1:
The patent makes the prediction system dynamic by using ML models that can adapt to changing conditions. Instead of static predictions, the system continuously learns from new data and adjusts its predictions, maintaining reliability in dynamic environments where factors affecting deviation are constantly changing.
Solution Approach 2:
The patent implements feedback mechanisms where ML models learn from actual outcomes and refine future predictions. The system uses reinforcement learning and continuous training on new data to improve prediction reliability over time, creating a closed-loop system that adapts to changing conditions.
3Device complexity
If human expertise is used for determining deviation reasons, then the system is simple to implement, but the productivity deteriorates due to inability to handle thousands of factors and terabytes of data at scale
Solution Approach 1:
The patent replaces human expertise with automated ML-based systems. The machine learning models automatically analyze thousands of factors and terabytes of data to determine deviation reasons, eliminating the bottleneck of manual analysis and enabling scaling to handle much larger and more complex datasets.
Solution Approach 2:
The patent enables the system to self-analyze and self-determine deviation reasons without human intervention. The ML models automatically process data, identify patterns, and generate insights, making the system self-sufficient in handling complex analysis tasks that would otherwise require human expertise.
4Device complexity
If traditional forecasting is done on a rolling basis, then the method is simple to implement, but the timeliness deteriorates causing deviation to be anticipated but not accurately predicted beforehand in time
Solution Approach 1:
The patent uses ML models to make predictions beforehand with greater accuracy and timeliness. By analyzing historical data and patterns, the system can predict deviations before they occur, allowing proactive rather than reactive responses. The ML models process data more quickly than traditional rolling forecasts, reducing the time loss.
Data Source
AI summary
A system and method for automatically predicting deviation on a metric of a use-case and deriving interconnections between metrics for generating action recommendations is provided. The system includes a deviation management system 104 which captures data from a plurality of external sources and internal sources and comprises of a deviation management platform 106 and a deviation management environment 108. The system includes various computation modules which work the deviation management platform 106 to provide a deviation management service to a set of clients that are associated with that service. The service and its users are specific to use-case, wherein the use-case is specified by a client device 116 inside the system. The system comprises of external data which is horizontal across a plurality of deviation management services and internal data which is specific to every deviation management service.