Tiered Trust Score Calculation Using Network and Transaction Data
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Solution Overview
Problem
Trust is a complex and elusive measure to quantify due to varying factors and preferences, and it can change over time, making it difficult to capture and utilize in decision-making processes.
Innovation Solution
A system and method for calculating trust scores that incorporate data from various sources, including network connectivity, credit scores, transaction history, and ratings, allowing for tiered trust scores (system, peer, and contextual) to be determined and updated based on specific interactions and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources and factors are incorporated into trust score calculation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The trust score calculation is divided into multiple independent components: system trust score (based on public information), peer trust score (based on network connections), and contextual trust score (based on transaction history). Each component can be calculated separately using specific data sources, making the overall complex system manageable through modular segmentation.
Solution Approach 2:
The patent introduces a hierarchical dimension to trust scoring by organizing scores into three levels: system-level (general reputation), peer-level (network-based), and contextual-level (transaction-specific). This dimensional organization allows complex trust assessment to be structured across multiple layers, improving measurability while managing complexity.
2Reliability
If trust score is updated dynamically based on recent trends and transactions, then reliability is improved, but loss of time increases
Solution Approach 1:
The system pre-calculates and stores component scores (network connectivity score, ratings score, transaction history score) as they become available. These pre-computed components can be quickly combined to generate updated trust scores, reducing the time required for dynamic updates while maintaining accuracy through continuous incorporation of new data.
Solution Approach 2:
The trust score system incorporates feedback loops where transaction outcomes and user interactions continuously refine the contextual trust score. Recent transactions and user feedback are weighted more heavily, allowing the system to adapt dynamically while using feedback mechanisms to optimize calculation efficiency through learned patterns.
3Adaptability or versatility
If different weightings are applied to data sources based on user preferences and transaction type, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system employs dynamic weightings that can be adjusted based on user preferences, transaction types, and contextual factors. Rather than using fixed weights, the system allows flexible configuration of component weights (e.g., emphasizing network connections for social trust vs. transaction history for financial trust), enabling adaptation to different scenarios while managing complexity through parameterization.
Solution Approach 2:
The patent changes parameters (weights) of the trust score calculation based on contextual conditions. Different transaction types (e.g., financial vs. social) trigger different weighting schemes, and user preferences can be encoded as parameter adjustments. This parameter-based adaptability allows versatility without requiring fundamentally different calculation systems.
Data Source
AI summary
Systems, devices, and methods are described herein for calculating a trust score. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations. A system trust score may be calculated for an entity by combining a variety of factors, including verification data, a network connectivity score, publicly available information, and/or ratings data. A peer trust score targeted from a first entity to a second entity may also be calculated based on the above factors. In some embodiments, the peer trust score may be derived from the system trust score for the target entity and may take into account additional factors, including social network connections, group/demographic info, and location data. Finally, a contextual trust score may be calculated between the first and second entities based on a type of transaction or activity to be performed between the two entities.