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 The work was posted as a preprint on arXiv¹ and has not yet been peer reviewed.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 co-author on the preprint. The tool estimates the probability that a paper uses “patent-like language”, he says.

Picking winners

The researchers trained TRI on 20,610 scientific papers, including 9,431 that had been matched to patents. Titles and abstracts were fed into five classifiers, with the best-performing model having a 78% chance of ranking a patent-linked paper above an otherwise comparable paper that was not linked to a patent.

Papers that were eventually cited in patents included vocabulary such as ‘prototype’, ‘device’ and ‘design’ more often than did papers that were not cited in patents. TRI analyses only titles and abstracts, so doesn’t directly assess a paper’s underlying data or results.

To test whether TRI’s highest-ranked papers were correlated with other markers of commercial activity, such as whether co-authors have industry affiliations and if authors had previously patented research, the researchers looked at the 100 highest TRI-ranked papers by authors at the University of Western Australia (UWA) in Perth. The papers, published between 2019 and 2026, were more likely than a random sample to show those markers: 83 of the 100 papers had industry-affiliated co-authors, and 34 involved at least one UWA-affiliated author who had previously patented research, McCarthy says. As a result, the team is now testing TRI with several universities.

McCarthy doesn’t recommend basing investment decisions on the tool’s results alone because it’s a probabilistic ranking. However, he says it might help to uncover “unexpected gems”.

Ben Miles, co-founder of Empirical Ventures, an early-stage deep-tech investment firm based in London, says the tool could be useful as an external signal for academics and funders wanting to decide which ideas deserve further support from universities, governments or philanthropies before they are mature enough for investors.

But patentable technologies aren’t always commercially viable — and so any measurement of that will always be imperfect for investors’ needs.

Spin-off scouts

TRI is one of several research-scouting tools that aim to identify promising science. Some of the tools are being adopted in research institutes to help identify discoveries that could — with backing — become a viable business.

One such tool, called Haystack, was built for the technology team at Cornell University in Ithaca, New York, to scan up to 13,000 papers per year — too many for Cornell’s technology-transfer team to inspect manually, says Matt Marx, who built the tool and is vice-provost for entrepreneurship, innovation and external engagement at the university.