Article by Northwestern Innovation Institute: “Universities produce a vast number of scientific publications each year. Yet only a small share ultimately leads to patents, startups, or broader industry adoption. The challenge is not a shortage of ideas, but limited visibility into which discoveries — and the researchers behind them — are most likely to move toward commercialization.
A new platform developed at the Northwestern Innovation Institute, called InnovationInsights, is designed to make that hidden potential visible.
Using artificial intelligence and large-scale research data, the system helps technology transfer offices identify faculty, papers, and emerging research areas with strong commercial promise — including many discoveries that would otherwise remain outside the innovation pipeline.
At the core of the platform is a searchable interface built around two levels of insight: researchers and their individual publications.
Users can explore researcher profiles that bring together key signals related to translational activity, including publication history, recent high-impact work, invention disclosures and whether a researcher’s papers have been cited by company patents. These profiles allow innovation teams to quickly identify faculty whose work is influencing industry or to show patterns associated with future commercialization.
At the publication level, InnovationInsights assigns each paper a commercial potential score based on machine-learning models trained on decades of historical data linking research outputs to downstream outcomes. Users can rank papers by this score to identify emerging discoveries that may be ready for translation, even before any patent activity occurs.
The platform also tracks citations from company patents, offering a direct view of where academic research is being used in industrial innovation. By comparing commercial potential scores with patent influence,institutions can see both future opportunity and current industry relevance…(More)”.
