Combinatorial Auction Solver Using Exchange Description Data
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Solution Overview
Problem
Combinatorial exchanges face computational intractability issues in determining winning bids that optimize seller revenue and minimize buyer cost, making efficient processing challenging.
Innovation Solution
A method involving a solver/analyzer that processes bids with associated exchange description data, including features like reserve price, non-price attributes, adjustments, objectives, constraints, and conditional pricing, to determine feasible allocations and maximize seller revenue while minimizing buyer cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If combinatorial exchanges use advanced exchange designs and expressive bidding to achieve best economic efficiency, then economic efficiency is improved, but computational complexity increases making winner determination computationally intractable
Solution Approach 1:
The patent segments the complex combinatorial bidding problem into multiple independent processing dimensions by introducing orthogonal features (reserve price, non-price attributes, adjustments, objectives, constraints) that can be evaluated separately. This allows the solver to handle each feature independently rather than processing the entire bid complexity simultaneously, making the winner determination computationally tractable while preserving economic efficiency.
Solution Approach 2:
The patent transforms the intractable combinatorial optimization problem into a tractable one by changing the parameter representation of bids. Instead of treating bids as monolithic complex objects, the system decomposes them into standardized parameters (price, attributes, constraints) that can be processed efficiently by the solver, maintaining the ability to achieve best economic efficiency while reducing computational burden.
2Productivity
If combinatorial exchanges process bids to maximize seller revenue and minimize buyer cost, then economic optimization is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-structuring bid data into standardized formats with predefined features (reserve price, non-price attributes, adjustments, objectives, constraints) before submission to the solver. This preprocessing organization allows the solver to quickly process bids without performing complex exploratory analysis, thereby reducing processing time while maintaining optimal economic outcomes for seller revenue and buyer cost.
Solution Approach 2:
The patent changes the parameter structure of bid processing from unstructured complex optimization to structured parameter evaluation. By representing bids in terms of discrete, evaluable parameters with clear mathematical relationships, the solver can efficiently compute optimal allocations without exhaustive search, significantly reducing processing time while achieving economic optimization goals.
Data Source
AI summary
A method of processing an exchange includes providing a solver/analyzer for determining a solution that includes at least one of a winning allocation and feasible allocations. At least one bid is received at the solver/analyzer, with each bid including at least one item and an associated price. Exchange description data (EDD) is associated with the at least one bid. The EDD is also received at the solver/analyzer. The processing of the at least one bid is modified in accordance with the at least one feature included in the EDD.


