Machine Learning Asset-Exchange Feedback for Offer Matching

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Solution Overview

Problem

Existing asset-exchange platforms suffer from inefficiencies due to differing views between offerors and offerees regarding the importance of offer terms, leading to unnecessary iterations, failed agreements, and suboptimal pricing.

Innovation Solution

An asset-exchange feedback system utilizing machine learning models to analyze historical data and generate attribute-importance scores, predicting optimal listing attributes and pricing to improve matching efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If offerors manually adjust offer terms to make offers more appealing, then the offer becomes more attractive to offerees, but the number of negotiation iterations increases and efficiency decreases

Engineering Contradiction:

Improveoffer appealVSAvoidnegotiation iterations

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of offeree preferences using machine learning models before the offer is made. Attribute-importance scores are calculated in advance based on historical data, allowing offerors to pre-configure optimal offer terms without needing multiple negotiation iterations to discover what offerees value.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements a feedback loop where outcomes of previous offers and negotiations are fed back into the machine learning models. This feedback continuously refines the attribute-importance scores, enabling the system to learn from past interactions and improve future offer configurations, reducing redundant negotiation cycles.

Inventive Principle:

Principle #23Feedback

2Reliability

If offerors significantly alter multiple terms to obtain acceptance, then the offer becomes more acceptable to offerees, but the complexity of offer management increases

Engineering Contradiction:

Improveoffer acceptanceVSAvoidoffer term management

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system identifies and focuses on specific local attributes that are most important to offerees based on attribute-importance scores. Rather than uniformly adjusting all offer terms, the system selectively modifies only the critical attributes that will have the greatest impact on acceptance, simplifying offer management while maintaining high acceptance rates.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically changes offer parameters based on calculated attribute-importance scores. The machine learning models determine which parameters should be adjusted and by how much, transforming the complex task of multi-term negotiation into a systematic parameter optimization process that reduces managerial complexity.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If offerors change terms without understanding offeree priorities, then the offer may become more appealing to the offeror, but it fails to resonate with the offeree's actual preferences

Engineering Contradiction:

Improveoffer configurationVSAvoidofferee preference alignment

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The machine learning system acts as an intermediary between offerors and offerees. It translates offeree preferences into quantifiable attribute-importance scores that guide offer configuration. This intermediary layer ensures that offer terms are aligned with offeree priorities while maintaining ease of offer creation for offerors.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces manual intuition and guesswork in understanding offeree preferences with machine learning-based analysis. By substituting the mechanical process of trial-and-error offer adjustment with algorithmic preference inference, the system preserves ease of operation while eliminating information loss about offeree priorities.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250292296A1Asset-Exchange Feedback In An Asset-Exchange Platform

Publication Date: 2025.09.18 LENDINGCLUB BANK NAT ASSOC

AI summary

An asset-exchange feedback system is implemented for performing asset-exchange feedback operations. The asset-exchange feedback system collects historical asset-listing data from an asset-exchange platform. The historical asset-listing data comprises, for each asset listing of a plurality of previous asset listings, a plurality of asset-listing attributes and a result of the asset listing. The asset-exchange feedback system uses a first machine learning model to determine, based on the historical asset-listing data, a first set of attribute-importance scores. Each attribute-importance score in the first set of attribute-importance scores corresponds to a respective asset-listing attribute in the plurality of asset-listing attributes and indicates an importance of the respective asset-listing attribute to one or more offerees participating in the asset-exchange platform. The asset-exchange feedback system performs an asset-exchange feedback operation based on the first set of attribute-importance scores.