Estimating technology performance improvement rates by mining patent data
研究了30种技术的专利数据，发现专利发明在整体引用网络中的中心度能准确可靠地预测技术性能的年改进率，并提出了标准化方法以消除专利动态变化的干扰。
The future direction of technology development depends on the relative yearly rate of functional performance improvement of different technologies. We use patent data to identify accurate and reliable predictors of this rate for 30 technologies. We illustrate how patent-based predictors should be normalized to correct for possible confounding factors introduced by changing patenting dynamics. We test the accuracy and reliability of various predictors by means of a Monte Carlo cross-validation exercise. We find that a measure of the centrality of domains’ patented inventions in the overall US patent citation network is an accurate and highly reliable predictor of improvement rates.