AI Negotiation Ranking for Anonymous Buyer-Seller Matching
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Solution Overview
Problem
Existing online trading platforms lack effective negotiation assistance, failing to address the complex negotiation aspect that can make or break deals, and often maintain buyer and seller anonymity, limiting direct interaction.
Innovation Solution
An AI-based negotiation system that suggests matching anonymous buyers and sellers using adaptive scoring and ranking, integrating user-defined and system-defined weighting factors, and employing dynamic data retrieval to optimize decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If buyer and seller anonymity is maintained to prevent direct negotiation, then user privacy is protected, but negotiation effectiveness and deal closure rate deteriorate
Solution Approach 1:
The patent introduces an AI negotiation assistant as an intermediary that mediates between anonymous buyers and sellers. The system preserves anonymity while enabling effective negotiation by having the AI assistant represent each party, analyze negotiation positions, suggest concessions, and guide the negotiation process without requiring direct human-to-human interaction. This resolves the contradiction by maintaining privacy protection while improving deal closure rates through intelligent mediation.
Solution Approach 2:
The system enables self-service negotiation where parties can configure their own AI negotiation assistants with their preferences, constraints, and negotiation strategies. The AI assistants then autonomously conduct negotiations on behalf of their users, allowing parties to maintain anonymity while having their negotiation interests actively represented and pursued without continuous human intervention.
2Measurement precision
If comprehensive data is retrieved for negotiation analysis, then decision accuracy is improved, but processing latency increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing negotiation data, user profiles, historical transaction data, and market information before negotiations begin. This pre-computation and data preparation work is done in advance so that during actual negotiations, the AI can quickly retrieve and analyze relevant information without experiencing latency, thus maintaining both high decision accuracy and fast response times.
Solution Approach 2:
The patent segments the comprehensive negotiation data into multiple categories and layers (e.g., user preferences, market data, historical transactions, product specifications). The AI negotiation assistant selectively retrieves only the relevant segments needed for each specific negotiation situation rather than processing all available data, reducing processing latency while maintaining decision accuracy by focusing on pertinent information.
Data Source
AI summary
An artificial-intelligence-based decision-assisting system and method are disclosed for generating ranked recommendations through adaptive multi-source analysis. The system includes a processor and a non-transitory computer-readable storage medium storing executable instructions that implement a scoring engine and a ranking engine. Decision parameters and user-defined weighting factors are received from user devices and combined with system-defined weighting factors retrieved from behavioral, historical, external-context, and scoring-criteria databases. Composite decision scores are computed and used to rank candidate options. The system iteratively updates weighting factors or rankings based on feedback data and dynamically restricts data retrieval to relevant parameters to reduce latency and improve throughput. Results are displayed via a graphical user interface, and anonymization procedures protect user identity. The method supports concurrent processing and adaptive learning to refine decision predictions.