System and Method for Agent-Driven Property Valuation and Incentive-Based Real Estate Scoring
卷宗概要
申请人
Ari Kyle Levine; Jason Ling; Jamie Tian; Joshua Spooner
发明人
Ari Kyle Levine; Jason Ling; Jamie Tian; Joshua Spooner
IPC 分类
CPC 分类
A distributed computing system and method for evaluating real estate agent performance through cryptographically secured, GPS-validated estimate submissions, scored by a machine learning-optimized algorithm with dynamically adjusted weightings. The Agent Competency and Credibility Score (ACCS) provides a transparent, technically verified trust metric combining price accuracy, geospatial expertise verification, property-type specialization, and statistical confidence calibration. The system implements a novel hybrid data architecture that maintains sensitive estimation data in secure encrypted databases while utilizing blockchain technology exclusively for transparent reward distribution, solving critical technical challenges in data security, computational efficiency, and incentive alignment.
原文（中文）
A distributed computing system and method for evaluating real estate agent performance through cryptographically secured, GPS-validated estimate submissions, scored by a machine learning-optimized algorithm with dynamically adjusted weightings. The Agent Competency and Credibility Score (ACCS) provides a transparent, technically verified trust metric combining price accuracy, geospatial expertise verification, property-type specialization, and statistical confidence calibration. The system implements a novel hybrid data architecture that maintains sensitive estimation data in secure encrypted databases while utilizing blockchain technology exclusively for transparent reward distribution, solving critical technical challenges in data security, computational efficiency, and incentive alignment.