The patent information community is witnessing the development of powerful machine learning algorithms (AI-driven software) designed to revolutionize patent searching. The tools are new, but have improved the quality and comprehensiveness of prior art searches that are done by human analysts.
This is a welcome development as no prior art search is complete and most cite only the best available relevant art.
None is complete because the prior art searching depends on the limited capacity of humans to find and sift through numerous records from a body of millions and to assess technological relevance under time constraints. This is easily proven by giving two independent patent analysts an identical search request. Each will cite a different set of prior references, with some crossover, nearly every time.
As trained patent search algorithms, these inexhaustible programs conduct millions of mathematical calculations to determine the relevancy of prior art. They compare the meaning of words and topics in a document and determine how closely a prior art reference within its dataset matches the invention disclosure or the patent upon which the search is based. Then, they are refined to provide more accurate results as they iterate over a known dataset.
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