Tabular Knowledge Extraction for Faster End-of-Lease Predictions
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Solution Overview
Problem
Traditional prediction models struggle to accurately and efficiently process large volumes of tabular data associated with end-of-lease actions, leading to inefficiencies in business decision-making for organizations leasing products.
Innovation Solution
A prediction system employing a knowledge extractor and noise remover that performs data preprocessing, applies dimensionality reduction and tabular attention models to generate knowledge vectors, and uses custom loss functions to improve model robustness for generating end-of-lease predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional prediction models process large volumes of tabular data, then prediction coverage is improved, but processing efficiency deteriorates
Solution Approach 1:
The patent extracts and removes noise from tabular data before processing. The noise removal module identifies and eliminates irrelevant or erroneous data points, retaining only the meaningful information needed for accurate predictions. This extraction approach allows the system to work with a refined subset of data, improving processing efficiency while maintaining prediction coverage.
Solution Approach 2:
The patent segments the prediction system into distinct functional modules: data preprocessing, noise removal, feature selection, and prediction generation. This segmentation allows each module to process and optimize specific aspects of the data independently, improving overall processing efficiency while handling large volumes of tabular data through specialized sub-processes.
2Measurement precision
If traditional prediction models process large volumes of tabular data, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by preprocessing the tabular data before main prediction processing. This includes data cleaning, normalization, and noise removal in advance, so that when the actual prediction occurs, the data is already optimized. This preliminary preparation maintains prediction accuracy while significantly reducing the time required for the core prediction process.
Solution Approach 2:
The patent extracts and removes noise and irrelevant information from the tabular data before processing. By eliminating distracting or erroneous data points in advance, the system can focus computational resources on the meaningful data, thereby maintaining high prediction accuracy while reducing overall processing time.
3Quantity of substance
If noise is present in tabular data, then data completeness is improved, but prediction reliability deteriorates
Solution Approach 1:
The patent converts the harmful effect of noise in the data into a benefit by implementing a specialized noise removal module that identifies and eliminates only the harmful noise while preserving valuable information. This approach maintains data completeness by retaining meaningful data points while removing only the elements that degrade prediction reliability, effectively converting the presence of noise from a liability into an opportunity for refined data processing.
Data Source
AI summary
Techniques described herein relate to a method for performing knowledge extraction and noise removal for prediction models. The method includes obtaining, by a prediction system, live tabular data; in response to obtaining live tabular data: performing data preprocessing on the live tabular data to generate processed live tabular data; generating a knowledge vector based on the processed live tabular data using a dimensionality reduction model and a tabular attention model; generating a prediction using a prediction model and the knowledge vector; and providing the prediction to a client; wherein the client performs prediction processing using the prediction.