Auction Pricing Optimization via Spatial-Temporal Data Segmentation
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Solution Overview
Problem
Conventional auction pricing systems fail to accurately determine the value of used vehicles due to data scarcity, variability in data quality, and the inability to consider spatial and temporal factors, leading to inaccurate pricing and missed opportunities for asset owners and dealers.
Innovation Solution
A predictive pricing system that utilizes advanced data science methods and an optimization engine to analyze historical auction data, incorporating spatial and temporal factors, to determine the optimal auction price for vehicles by processing data through a networked platform, providing dealers with tools for inventory management and profit optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional simplified valuation mechanisms are used, then processing is faster and less expensive, but pricing accuracy deteriorates due to inability to consider spatial and temporal factors
Solution Approach 1:
The patent segments the valuation process into distinct modules: data collection from multiple sources, data processing and cleaning, spatial factor analysis, temporal factor analysis, and prediction model execution. This segmentation allows the system to handle complex analyses while maintaining processing efficiency through parallel computation of different factor types.
Solution Approach 2:
The patent introduces spatial (geographic location) and temporal (time-based) dimensions to the traditional one-dimensional valuation approach. By adding these dimensions, the system captures location-specific demand variations and time-based market dynamics, significantly improving pricing accuracy without proportionally increasing processing complexity.
2Measurement precision
If comprehensive data from multiple sources is processed, then pricing accuracy improves, but computational power, memory, and data storage requirements increase
Solution Approach 1:
The patent extracts and separates critical valuation factors from comprehensive data sets, focusing computation on the most influential spatial and temporal parameters. By identifying and extracting key features rather than processing all raw data, the system maintains high pricing accuracy while reducing computational burden and resource requirements.
Solution Approach 2:
The patent performs preliminary data processing, cleaning, and validation before main analysis. Historical data is pre-processed and stored in optimized formats, allowing the prediction model to execute faster with reduced computational requirements during actual valuation operations.
3Ease of operation
If fixed-level vehicle grouping is used, then valuation is simpler and faster, but accuracy deteriorates due to data scarcity for older vehicles
Solution Approach 1:
The patent implements dynamic vehicle grouping that adapts based on vehicle age, data availability, and market conditions. For older vehicles with limited data, the system dynamically adjusts grouping granularity and incorporates spatial-temporal factors to compensate for data scarcity, maintaining valuation accuracy while keeping the process manageable.
Data Source
AI summary
An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.