Electronic Marketplace Group Bid Allocation
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Solution Overview
Problem
Existing electronic marketplaces and auction systems are limited in allocating resources to maximize collective value among bidders, often favoring only the highest bidders and lacking efficient mechanisms for grouping users to share resources effectively.
Innovation Solution
A method and system that organizes task requests into groups, determines group bids by multiplying individual bids with multipliers representing comparative value, and automatically groups bidders to win resources, allowing transparent participation and enhanced pricing mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If only the highest bidder is allowed to win the auctioned item, then the allocation efficiency is improved, but the collective value among all bidders deteriorates
Solution Approach 1:
The patent segments the single winning bid into multiple group bids, where each group represents a collection of individual bidders. Instead of allocating the resource to one highest bidder, the system divides bidders into groups and allocates resources to multiple winning groups, thereby distributing the resource output to multiple parties while maintaining allocation efficiency through automated grouping based on bid values.
Solution Approach 2:
The patent merges multiple individual bidders into groups, where each group acts as a unified bidding entity. By combining individual bids into group bids and allowing multiple groups to win simultaneously, the system preserves the competitive aspect of auctions while enabling multiple beneficiaries to share in the resource output, thus increasing collective value.
2Quantity of substance
If multiple bidders are grouped together to win resources, then the collective value is improved, but the system complexity deteriorates
Solution Approach 1:
The system implements self-service through automated grouping algorithms that automatically organize individual bidders into groups based on their bid values and resource requirements. The computer system autonomously determines group compositions, calculates group bids by aggregating individual bids within groups, and identifies winning groups without requiring manual intervention, thereby managing complexity through automation rather than increasing it.
Solution Approach 2:
The patent changes the parameter of bid evaluation from individual bid amounts to group bid totals. By transforming the bidding mechanism to evaluate groups rather than individuals, the system simplifies the complexity of managing multiple individual allocations while preserving the ability to capture collective value through aggregated group bids.
3Loss of information
If group participation is made transparent to individual bidders, then the privacy is improved, but the bidding process complexity deteriorates
Solution Approach 1:
The patent extracts the grouping information from the individual bidder's perspective, allowing bidders to participate without knowing their specific group composition or other members' identities. The system processes grouping logic separately and independently from individual bidding actions, thereby protecting privacy while maintaining the functional integrity of the bidding process through automated backend processing.
Data Source
AI summary
In one embodiment, a method for an electronic auction transaction includes organizing by a computer each of a plurality of task requests into one or more respective groups. Each task request corresponds to a respective desired use of one or more resources. In addition, each task request has a respective task bid. The computer determines a group bid for each group at least in part by comparing the products calculated by multiplying each first bid within the group by a respective multiplier. The group bid is indicative of a collective value associated with the task bids of the group. The respective multiplier is a number indicative of the comparative value of the task bid relative to the other task bids in the group. The computer determines one or more winning group bids by comparing the group bids to each other.