Auction Result Prediction Using Neural Network Analysis
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Solution Overview
Problem
Online auction platforms face challenges in predicting auction outcomes due to variations in item condition, shipping options, seller ratings, item descriptions, and auction timing, leading to inaccurate price estimations and discouragement of buyers and sellers.
Innovation Solution
Auction analysis system that retrieves and derives item, seller, and auction characteristics from prior auctions, using a processor and memory to provide predicted end-of-auction prices, ranges, and thresholds based on historical data and seller-input characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple price averaging is used to estimate auction results, then the estimation process is simple and quick, but the prediction accuracy is relatively low
Solution Approach 1:
The system transforms the single-parameter approach (simple price averaging) into a multi-parameter analysis model that incorporates item characteristics, seller characteristics, auction characteristics, and historical data. This parameter expansion enables accurate predictions while maintaining computational efficiency through structured data processing.
Solution Approach 2:
The patent replaces the mechanical calculation method (simple averaging) with an intelligent prediction system using neural networks and machine learning algorithms. This substitution allows the system to process complex relationships between multiple factors and generate accurate predictions without proportionally increasing computational complexity.
2Measurement precision
If detailed item characteristics and historical data are analyzed, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex prediction task into distinct modular components: data collection module, data processing module, neural network prediction module, and result output module. Each module handles specific aspects of the analysis, making the overall system more manageable and maintainable while achieving high prediction accuracy through comprehensive data analysis.
Solution Approach 2:
The patent introduces intermediate data structures and processing layers that mediate between raw input data and final predictions. These intermediaries organize and preprocess information before feeding it to the neural network, reducing the complexity burden on the core prediction algorithm while enabling thorough analysis of detailed characteristics.
3Reliability
If comprehensive auction data is collected and analyzed, then prediction reliability improves, but data processing time increases
Solution Approach 1:
The system performs preliminary data collection, cleaning, and organization before the actual prediction process. Historical auction data is pre-processed and stored in optimized structures, allowing the neural network to receive ready-to-use features during prediction. This preliminary preparation ensures reliable predictions based on comprehensive data while minimizing real-time processing delays.
Data Source
AI summary
An auction analysis system predicts auction results. The analysis system may determine item, seller, or auction characteristics from prior or pending auctions. The analysis system also obtains item characteristics of an item for which a result prediction is sought, either by a buyer or by a seller. A price predictor in the system accepts the auction and item characteristics and predicts an auction result based on the characteristics.