Magee and his colleagues found that by looking for patent overlap between the U.S. To find the patents that best represent a domain, the team built on previous research conducted by co-author Chris Magee, a professor of the practice of engineering systems within the Institute for Data, Systems, and Society (IDSS). To accomplish this, the researchers developed a method using a new probability-based algorithm, machine learning, natural language processing, and patent network analytics.Ī technology domain, as the researchers define it, consists of sets of artifacts fulfilling a specific function using a specific branch of scientific knowledge. The major purpose of this new study is to provide predictions of the performance improvement rates for the thousands of domains not accessed by empirical measurement. ![]() “In some large technological fields, including software and clinical medicine, such measures have rarely, if ever, been made.”Ī previous MIT study provided empirical measures for 30 technological domains, but the patent sets identified for those technologies cover less than 15 percent of the patents in the U.S. “The rate of improvement can only be empirically estimated when substantial performance measurements are made over long time periods,” says Anuraag Singh SM ’20, lead author of the paper. patent system as a set of 1,757 discrete technology domains, and quantitatively assesses each domain for its improvement potential. The study describes 97 percent of the U.S. New research from MIT aims to assist in the prediction of technology performance improvement using U.S. ![]() For decision-makers like investors, entrepreneurs, and policymakers, predicting which technologies are fast improving (and which are overhyped) can mean the difference between success and failure. ![]() The pace of that technological change can affect its impact, and how quickly a technology improves in performance can be an indicator of its future importance. The societal impacts of technological change can be seen in many domains, from messenger RNA vaccines and automation to drones and climate change.
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