Automated Reverse Auction Solver for Dynamic Buyer Seller Matching
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Solution Overview
Problem
Current reverse auction systems require manual negotiation between buyers and sellers, failing to fully leverage automation and optimization, leading to inefficiencies and subjectivity in matching purchase parameters and optimizing prices.
Innovation Solution
A fully automated system that dynamically ranks and matches buyers and sellers through an Automated Reverse Auction Solver and Inventory Standardization Service, using standardized input formats to optimize parameter matching and price calculation without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual negotiation is used between buyers and sellers, then flexibility in dealing with complex purchase parameters is improved, but automation level and efficiency deteriorate
Solution Approach 1:
The system enables self-service through automated algorithms that independently match buyers and sellers based on purchase parameters without requiring manual negotiation. The optimization algorithm autonomously processes auction data, evaluates bids, and determines winning bids, allowing the system to serve itself rather than relying on human intervention for each transaction.
Solution Approach 2:
The patent replaces the mechanical system of manual human negotiation with an automated computational system. The optimization algorithm acts as a substitute for human negotiators, using mathematical models and data processing to automatically match purchase parameters, evaluate seller bids, and determine optimal transactions, thereby eliminating the need for manual mechanical negotiation processes.
2Adaptability or versatility
If manual negotiation is used between buyers and sellers, then subjective judgment can be applied, but objectivity and consistency deteriorate
Solution Approach 1:
The system implements feedback mechanisms where the optimization algorithm continuously receives data from auction outcomes, purchase parameter specifications, and seller bid responses. This feedback loop allows the system to learn from past transactions and consistently apply objective criteria, improving measurement precision by using standardized evaluation metrics that eliminate subjective variability while maintaining adaptability through data-driven adjustments.
Solution Approach 2:
The patent transforms subjective judgment into objective parameter-based evaluation by changing the system's operating parameters from human discretion to quantifiable metrics. The optimization algorithm evaluates bids based on standardized parameters such as price, delivery time, and quality specifications, converting previously subjective negotiation factors into measurable, consistent criteria that improve objectivity while maintaining the ability to adapt to different purchase requirements.
3Productivity
If automated algorithms are used to match buyers and sellers, then efficiency is improved, but handling of complex purchase parameters deteriorates
Solution Approach 1:
The system segments complex purchase parameters into distinct, manageable components that can be processed independently by the optimization algorithm. By dividing the evaluation process into separate parameter assessments (such as price, quality, delivery time, and quantity), the system maintains high efficiency while effectively handling complexity, as each segmented parameter can be evaluated using specialized sub-routines within the overall optimization framework.
Solution Approach 2:
The patent adds another dimension to parameter handling by implementing a multi-dimensional optimization approach. The system evaluates purchase parameters across multiple dimensions simultaneously, using advanced algorithms that can process high-dimensional data spaces. This dimensional expansion allows the automated system to handle complex, multi-factor purchase parameters efficiently by treating them as vectors in a higher-dimensional solution space rather than sequential constraints.
4Loss of time
If fully automated reverse auctions are implemented, then time consumption is reduced, but system complexity increases
Solution Approach 1:
The system applies preliminary action by pre-configuring optimization parameters, buyer requirements, and seller profiles before auctions begin. The optimization algorithm is pre-loaded with evaluation criteria and purchase parameter specifications, allowing it to immediately process auction data without requiring complex real-time decision-making. This preliminary preparation reduces time consumption during actual auctions while managing system complexity through advance setup rather than complex runtime processing.
Data Source
AI summary
Provided for is a system for dynamically ranking buyers and sellers in an auction comprising granting access to one or more buyers on the computer system; assigning a seller permission; assigning a buyer permission; providing, to the seller and the buyer, a platform; receiving, via the platform, one or more desired items; receiving a plurality of parameter fields; populating each of the plurality of parameter fields with one or more parameter values; receiving the plurality of parameter fields and one or more parameter values from at least one of the buyer and seller; standardizing the one or more parameter values and the plurality of parameter fields into one or more Stock Standard Form (SSF) details; initiating an Inventory Standardization Service (ISS); initiating an optimization; and calculating a final price for the one or more desired items.