Investors and technology-transfer offices expend enormous effort trying to spot commercially promising research before it reaches the point of patenting. Now, a machine-learning tool is aiming to speed up this process by scoring how 'patent-like' a scientific paper is ' months or years before any deal, patent filing or spin-off company reveals its commercial potential. And it's one of many proffering the same capability. The tool, called the Translation Readiness Index (TRI), performs a linguistic analysis of a paper's title and abstract. It then measures how similar a paper's vocabulary is to publications that have previously been paired with patents. The method was developed by researchers at the data-analytics firm League of Scholars in Sydney, Australia. The work was posted as a preprint on arXiv1 and has not yet been peer reviewed. 'It's a new way of triaging or ranking' research, says computational social scientist Paul McCarthy, co-founder of League of Scholars and a...
learn more
