FedScoop – “New search algorithms for relevant prior art most excite the U.S. Patent and Trademark Office’s CIO right now. USPTO created the machine-learning algorithms to increase the speed at which patents are examined by importing relevant prior art — all information on its claim of originality — into pending applications sent to art units, said Jamie Holcombe. Filtering data into haystacks allowing patent examiners to more easily find what they’re looking for — the needle — is the new paradigm for search algorithms, Holcombe said. “The ability to search, especially the big datasets, gets me so excited,” he added, during an ACT-IAC event Tuesday. “Because that means we can unleash that power to anybody who can get on a computer and access the net.” Patent examiners previously had to scour three to four pages of single-spaces, text searches for relevant prior art assembled based on word relevance. Now examiners can search concepts like “chemical adhesion” and receive all the relevant prior art they need in one place…”