Event Prediction Using Gradient Boosting for Order Lead Time
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Solution Overview
Problem
Current event prediction systems face challenges in accurately determining order preparation time due to the complexity of analyzing large numbers of factors and the inefficiency of traditional data-processing tools, which can lead to suboptimal customer satisfaction and operational management in food service entities.
Innovation Solution
The implementation of a machine learning process using a gradient boosting algorithm to predict order preparation time by analyzing a set of properties associated with historical customer orders, including characteristics and real-time conditions, allowing for more accurate and efficient processing of large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data-processing tools are used to analyze factors for event prediction, then the system structure is simple, but the prediction accuracy deteriorates due to inability to handle large numbers of factors efficiently
Solution Approach 1:
The patent replaces traditional mechanical data-processing tools with a machine learning-based event prediction system. The system uses trained machine learning models that automatically process and analyze large numbers of factors and data points, substituting manual or rule-based processing with intelligent algorithms that can handle complexity while maintaining accuracy.
Solution Approach 2:
The patent transforms the prediction system by changing parameters from fixed rule-based thresholds to dynamic machine learning-derived parameters. The system trains models on historical data to optimize prediction parameters, allowing the system to adapt to varying conditions and improve accuracy while managing complexity through automated parameter optimization.
2Ease of operation
If hierarchical processing is used to determine possible outcomes, then the decision-making process is structured, but the processing time increases due to multiple analysis levels
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical event data before actual prediction needs arise. The system performs offline training and model preparation in advance, so that when predictions are needed, the pre-trained models can quickly process new data without requiring time-consuming hierarchical analysis at prediction time.
Solution Approach 2:
The patent creates simplified copies of complex hierarchical processing through machine learning models. Instead of executing multiple levels of hierarchical analysis for each prediction, the system uses trained models that have learned patterns from historical data, providing structured decision-making capabilities with significantly reduced processing time.
3Loss of information
If feature-level modeling is used in machine learning, then the model is interpretable, but the prediction accuracy deteriorates due to loss of granular data information
Solution Approach 1:
The patent transitions from traditional feature-level modeling to a more granular dimension by utilizing raw event data and detailed property information. The machine learning models process data at a finer granularity level, incorporating multiple properties of events rather than aggregated features, thereby retaining more information while using advanced modeling techniques to manage the increased complexity.
Data Source
AI summary
In an aspect, a method of event prediction using a predictive process is presented. The method includes receiving, at a computing device, a digital request from an order management system of an entity. The method includes obtaining, by the computing device, at least a property of the digital request, wherein the property includes a time to complete the digital request. The method includes inputting, by the computing device, the property into the predictive process. The predictive process utilizes an extreme gradient boosting function and is trained to input properties and output prediction events through at least an iteration of a training phase of the extreme gradient boosting function. The method includes generating, from the predictive process, a prediction event including a period of time. The prediction event is indicative of a time to complete the digital request.