Global Conduct Score Aggregation for Online Transaction Trust
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The anonymity of the internet hinders the ability of parties in online commercial transactions to assess each other's backgrounds, leading to uncertainties in trust and risk assessment.
Innovation Solution
A system that generates and processes global conduct scores and attribute data to evaluate individuals' behavior across networks, using a scoring server that aggregates normal actor scores from multiple sites to provide a comprehensive assessment for transaction decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If internet-based commercial transactions are conducted with anonymity, then ease of operation and accessibility are improved, but trust and reliability deteriorate due to inability to assess backgrounds
Solution Approach 1:
The patent introduces a third-party scoring system that acts as an intermediary between anonymous trading parties. The scoring server collects data from multiple sources (banks, telecommunications companies, etc.) and generates conduct scores that serve as trusted mediators for risk assessment, enabling transactions without direct background checks between parties.
Solution Approach 2:
Instead of requiring direct knowledge of a party's background, the system creates a simplified copy of conduct information in the form of a conduct score. This score aggregates and summarizes behavioral data from multiple sources, providing a manageable representation of trustworthiness that can be shared between parties without exposing sensitive personal information.
2Reliability
If comprehensive background assessment is performed, then reliability and trust are improved, but device complexity and processing requirements worsen
Solution Approach 1:
The patent segments the complex background assessment task into multiple independent data collection points (banks, telecommunications companies, etc.), each contributing specific behavioral data. The scoring server then aggregates these segmented data sources into a unified conduct score, distributing complexity across multiple sources rather than requiring a single monolithic assessment system.
Solution Approach 2:
The system transforms complex, multi-dimensional background data into a simplified parameter - the conduct score. By converting diverse data from multiple sources into a single standardized metric, the system reduces processing complexity while maintaining comprehensive assessment capability. The score serves as a condensed representation that can be easily processed and compared.
3Measurement precision
If data from multiple sources is aggregated, then measurement precision and assessment accuracy are improved, but loss of information and data management complexity worsen
Solution Approach 1:
The patent extracts the essential conduct information from multiple data sources and separates it from the surrounding data noise. By identifying and extracting only the behaviorally relevant data points from banks, telecommunications companies, and other sources, the system achieves accurate conduct assessment while managing information volume through selective extraction rather than comprehensive data collection.
Data Source
AI summary
In one embodiment, a system and method is illustrated as including generating a model using at least one of a global conduct score and attribute data. A range of numeric values may be retrieved, based upon which, a better term is granted in a transaction than would otherwise be granted in the transaction. A comparison may be made between the model and the range of numeric values. Further, a better term may be granted in the transaction where the model falls within the range of numeric values. The global conduct score is computed using an approach including at least one of a feed score approach, and a composite score approach. The attribute data includes page view data, click through data, account usage data, and good purchased data. The model includes a global conduct score model, a weighting model, an AI based model, and an associated network based model.