Method and system for probabilistically quantifying and visualizing relevance between two or more citationally or contextually related data objects

This invention describes a computer program designed to find and rank related documents, such as patents, from a large collection. It works by analyzing how documents cite each other, specifically tracking these connections across three to five generations. The program then calculates a relevance score for each document by adding up individual scores derived from these citation links.

Why it matters: Filed before the widespread adoption of advanced graph databases and large language models. These technologies now make it significantly easier to build and scale systems that analyze complex citation networks and probabilistically quantify contextual relevance with far greater accuracy than was feasible in 2014.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, data indexing, probabilistic models

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