The front-end of the R&D process in virtually every industry relies on effective literature search, which after decades of stagnation is now, finally, getting significantly better. That’s because machine learning has entered the picture, enabling some tasks historically performed solely by information specialists to now be augmented and levered by computers.
For example, machine learning enables the automated generation of document summaries based on an analysis of the important ideas in a document. A researcher can glean the important insights without having to download and read a document, resulting in much faster business research – and a corresponding gain in productivity.
A second (and very significant) business research application of machine learning is the “More Like This” button, similar to what we’re used to seeing on consumer shopping websites. Machine learning-based “More Like This” functionality lets a user scan a search result – typically a list that’s too broad for the user’s actual purpose, because most user queries are too general – and, once they have found an on-point hit, invoke the machine’s intelligence with a single mouse click to rewrite the search query based on an in-depth semantic analysis of the on-point document, which produces an entire search result of highly relevant material.
Perhaps the most transformational and impactful machine learning-enabled capability is the advent of the automated search report, or “Insights Report”. No more slogging through search results, downloading and scanning documents, going back to the search result and looking for more. The user can let the machine read the documents; then the user just reads the Insights Report generated by the machine, which tells the researcher what it finds that is pithy, relevant, and important.
The business and technical content that can be mined automatically in this way for the benefit of R&D is vast. The net effect is to accelerate the literature research phase of a R&D project and get good ideas into development sooner. Time-to-completion, time-to-publish, time-to-market are all cut – and the research result is better informed and more likely to produce breakthroughs.[This post is based on an article written by Northern Light CEO C. David Seuss, which was published recently in New Equipment Digest.]