Adaptive Collaborative Matching Platform for High-Value Assets
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Solution Overview
Problem
The high-value asset market lacks a centralized, collaborative platform for sharing and matching data between buyers and sellers, leading to siloed information and limited market awareness, hindering effective transactions across geographical boundaries.
Innovation Solution
A normalized, adaptive collaborative matching platform that collects, normalizes, and encrypts data to match buyer affinities with available assets by using machine learning techniques to correlate lifestyle scores and attributes, enabling bi-directional matching and continuous refinement based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a centralized collaborative platform is implemented, then information sharing and matching efficiency are improved, but system complexity and data security requirements increase
Solution Approach 1:
The platform segments data into structured empirical data and unstructured data, processes them through separate normalization pathways, and combines them for matching. This segmentation allows complex data types to be handled systematically, improving matching efficiency while managing platform complexity through modular data processing architecture.
Solution Approach 2:
The patent introduces a normalization engine as an intermediary component that standardizes data from multiple sources before matching. This mediator handles the complexity of data integration centrally, enabling efficient matching across the platform while encapsulating the complexity within a dedicated component rather than分散 across the entire system.
2Measurement precision
If comprehensive data collection and normalization are performed, then matching accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The platform performs preliminary normalization of data during the data collection phase, converting unstructured data to structured empirical data and appending third-party data sources in advance. This preliminary processing reduces the computational burden during actual matching operations, improving matching accuracy while minimizing processing time delays when matches are needed.
Solution Approach 2:
The normalization engine operates continuously to maintain a repository of normalized structured empirical data and appended third-party data. By maintaining this pre-processed data continuously available, the system avoids repeated processing of raw data, thereby improving matching accuracy while reducing redundant processing time and computational resource consumption.
3Adaptability or versatility
If proprietary client information is shared across the network, then collaborative matching capability is improved, but information security and client privacy concerns increase
Solution Approach 1:
The patent extracts and separates personally identifiable information from client profiles, maintaining only the necessary lifestyle and preference data needed for matching. This extraction allows collaborative matching to proceed with anonymized or pseudonymized data, improving collaborative capability while reducing information security risks and client privacy concerns by removing sensitive identifiers from the shared data pool.
Data Source
AI summary
An adaptive collaborative platform applies various machine learning techniques to correlate potential purchasers with high-value articles of property that may be of interest. Attributes, characteristics, preferences, and the like of a potential purchaser are scored against attributes and features of articles. The platform learns from interaction by the agents and the potential purchasers and adapts to become more attuned to the desires and lifestyle of purchasers and to gain more and more pertinent information from the listing agents regarding high-value articles, so as to ultimately to arrive at a better match between a high value article for sale and a likely purchaser.