Blockchain Auction Pricing for Digital-Goods Demand Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Sellers face challenges in determining the optimal price for digital goods to maximize sales, as they struggle to guess whether selling at a higher price will result in fewer copies sold or a lower price will attract more buyers, leading to unsold items due to mismatched buyer expectations.
Innovation Solution
An auction system incorporating blockchain, smart contracts, and an algorithm that calculates the number of copies sold and the single item price dynamically, ensuring fair pricing for both buyers and sellers by allowing independent bids and providing live auction statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the selling price is set high, then the seller can maximize revenue per item, but the number of copies sold decreases
Solution Approach 1:
The patent implements a dynamic pricing mechanism where the selling price is not fixed but determined through an auction process. The algorithm dynamically adjusts the price based on buyer bids, allowing the system to find the optimal balance between quantity sold and revenue per item. This resolves the contradiction by making price flexible rather than static.
Solution Approach 2:
The system changes the price parameter through the auction process, allowing it to adapt to market conditions. By collecting bids and processing them through the algorithm, the system identifies the winning bid price that optimizes both the number of copies sold and the revenue per item, effectively changing the price parameter to resolve the contradiction.
2Quantity of substance
If the selling price is set low to attract more buyers, then the number of copies sold increases, but the revenue per item decreases
Solution Approach 1:
The auction-based dynamic pricing system allows the price to adjust based on actual buyer demand. Instead of setting a low fixed price, the system collects bids and determines the optimal price through the algorithm, which can result in higher revenue per item while still selling multiple copies if the market supports it.
Solution Approach 2:
The system incorporates feedback from buyer bids to determine the optimal selling price. By analyzing the submitted bids and using the algorithm to process them, the system receives feedback on what price points are acceptable to buyers, allowing it to optimize both quantity sold and revenue per item based on actual market response.
3Adaptability or versatility
If separate selling channels are created to offer flexible pricing, then pricing adaptability improves, but system complexity increases
Solution Approach 1:
The patent merges the auction functionality, bid collection, and price determination algorithm into a single integrated smart contract system. Instead of creating separate selling channels, the system combines multiple functions into one unified auction mechanism that handles pricing flexibility internally, reducing overall system complexity while maintaining adaptability.
Solution Approach 2:
The auction system serves multiple functions: it collects bids, determines winning prices, allocates items to buyers, and handles pricing decisions all within a single mechanism. This multi-functional approach provides pricing flexibility without requiring separate specialized channels for each function, thereby reducing system complexity.
Data Source
AI summary
Disclosed in the present invention an auction system incorporating blockchain and smart contracts and algorithm, which operates using block-chain, algorithm and smart contracts and non-fungible tokens and fungible tokens built on EVM compliant blockchains and off-chain service. The auction system that has the Auction Smart Contract deployed on all supported blockchain and the web pages that allow to create the auction, bid, store auction calculation, withdraw tokens, and move bought items to the wallet, and display auction statistics and a Statistics and Bridge Service that calculates each auction statistics and the result using the algorithm.