Trust Platform for Cross-Marketplace Review Normalization
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Solution Overview
Problem
Existing marketplaces lack a system to aggregate and normalize user reviews across platforms, limiting the portability and accuracy of trust scores, which affects user confidence and transaction success in person-to-person transactions.
Innovation Solution
A trust platform that aggregates, normalizes, and weights transaction review values from multiple marketplaces to generate a baseline individual trust score, considering transaction characteristics, and adjusts scores based on contextual analysis to improve accuracy and portability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If review systems are implemented within specific marketplaces, then feedback can be collected for transactions, but the reviews are not portable and do not translate to other marketplaces
Solution Approach 1:
The patent creates a universal trust score system that functions across multiple marketplaces. The trust score is designed to be platform-agnostic, allowing it to be applied universally across different transaction types and marketplaces while maintaining its core function of indicating user reliability. This resolves the contradiction by making the review system both specific enough to be meaningful and universal enough to be portable.
Solution Approach 2:
The patent introduces a trust score as an intermediary representation that mediates between specific marketplace reviews and general user trust assessment. Instead of directly porting raw reviews between marketplaces, the system converts reviews into a standardized trust score that serves as a mediator, preserving trust information while enabling cross-platform use.
2Measurement precision
If multiple transaction review values are aggregated, then a more comprehensive trust assessment is achieved, but the complexity of processing and normalizing reviews from different scales increases
Solution Approach 1:
The patent transforms review data from various marketplaces by changing its parameters - converting different rating scales (e.g., 5-star, 10-star, thumbs up/down) into a standardized trust score parameter. This parameter transformation simplifies the aggregation process while maintaining measurement precision, as all reviews are converted to a common metric before being combined.
Solution Approach 2:
The patent segments the complex task of trust assessment into distinct processing stages: collecting raw reviews, normalizing to standard scales, weighting based on relevance, and aggregating into a final trust score. This segmentation reduces processing complexity by breaking down the monolithic task into manageable, sequential steps that can be independently optimized.
3Measurement precision
If transaction review values are weighted based on multiple factors, then the trust score reflects transaction importance better, but the computational requirements and processing time increase
Solution Approach 1:
The patent applies partial weighting - not all review factors are weighted equally or processed with the same level of detail. The system identifies and weights only the most significant factors (e.g., transaction amount, user role, transaction type) while using simpler or uniform weighting for less critical factors. This partial action approach maintains accuracy for key determinants while reducing overall computational burden and processing time.
Data Source
AI summary
A trust platform receives transaction review values from a plurality of marketplace providers, each transaction review value associated with a person-to-person transaction. The platform weighs each transaction review value to generate a weighted transaction review value based on a characteristic of the transaction. A baseline individual trust score is generated based on an aggregation of the weighted transaction review values which reflects a “trust” attributable to a user. The trust platform is also configured to adjust new transaction review values based on the baseline individual trust score to render such review more accurate.