Multi-Source Trust Scoring For Geographic Entity Search
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Solution Overview
Problem
Trust is a complex and elusive measure to quantify, influenced by various factors and changing over time, making it difficult to capture and utilize effectively in social and business interactions.
Innovation Solution
A system and method for calculating trust scores based on system, peer, and contextual trust scores, incorporating verification data, network connectivity, ratings, and transaction history, which can be used to automatically make decisions and search for entities based on trustworthiness and geography.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple data sources and factors are incorporated to calculate trust scores, then the accuracy and reliability of trust assessment is improved, but the system complexity and difficulty of implementation increases
Solution Approach 1:
The trust score calculation system is segmented into multiple independent data sources (verification data, network connectivity, ratings data, group/demographic information, location data, transaction history) that can be collected, processed, and weighted separately. Each data source contributes a specific component to the overall trust score, allowing the complex assessment to be broken down into manageable modules that can be developed and maintained independently.
Solution Approach 2:
The trust score system is designed as a universal framework that can assess trustworthiness across multiple contexts and entity types (individuals, organizations, locations). The same core calculation mechanism handles different data sources and transaction types, making the system multi-functional and reducing overall complexity through reuse of common components across diverse applications.
2Measurement precision
If contextual factors and transaction-specific weights are applied to trust scores, then the precision of trust measurement for specific activities is improved, but the computational requirements and processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing base trust scores from static data sources (verification data, network connectivity, ratings data, group/demographic information) before specific transactions occur. When a transaction is initiated, the system only needs to retrieve the pre-calculated base scores and apply contextual adjustments, significantly reducing real-time computational requirements while maintaining high precision.
Solution Approach 2:
The system dynamically changes parameters by adjusting the weightings of different data sources based on the specific transaction context and activity type. Rather than recalculating entire trust scores from scratch, the system modifies only the relevant parameter weights for the current transaction, achieving context-specific precision with minimal additional processing time.
3Reliability
If real-time trust score updates are performed based on transaction history and new information, then the currency and relevance of trust data is improved, but the computational load and energy consumption increases
Solution Approach 1:
The system implements periodic action by updating trust scores at scheduled intervals and triggered by specific events rather than continuously. Base trust scores are updated periodically from static data sources, while transaction-specific updates occur only when relevant transactions are completed. This event-driven periodic update mechanism maintains currency of trust data while avoiding unnecessary continuous computation and energy consumption.
4Reliability
If comprehensive verification data and network connectivity information are collected, then the robustness of trust assessment is improved, but the data collection infrastructure and operational complexity increases
Solution Approach 1:
The system applies self-service by automatically collecting verification data and network connectivity information from multiple sources without requiring manual intervention. The trust score calculation system autonomously queries data sources, retrieves relevant information, and processes it into trust scores. This automation reduces operational complexity and makes comprehensive data collection manageable, as the system serves itself by gathering and processing data without extensive human oversight.
Data Source
AI summary
Systems, devices, and methods are described herein for searching for entities based on trust score and geography. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses/corporations and may take into account a variety of factors, including verification data, network connectivity, publicly available information, ratings data, group/demographic information, location data, and transactions to be performed, among others. A user may search for entities within a certain geographic location that meet a desired trust score. The results of the search may be generated for display on a user device, for example, by generating a map that shows the current location of the user device and the identified entities. The search may be filtered by entering an anticipated activity or transaction to be performed or desired by the user, and thereby returning entities that are associated with the activity or transaction.