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The Quantification of Injectability by Mechanical Testing
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Quantitative determination of technological improvement from patent data.

Christopher L Benson1, Christopher L Magee1

  • 1Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

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Summary

Patent information, including importance and recency, strongly correlates with technological progress. A combined patent metric effectively predicts performance improvements over a decade, offering practical estimation value.

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Area of Science:

  • Innovation Studies
  • Technology Management
  • Intellectual Property

Background:

  • Technological progress is crucial for economic growth and societal advancement.
  • Patents serve as a key indicator of innovation within specific technological domains.
  • Quantifying the relationship between patent characteristics and the pace of technological advancement is essential for strategic planning.

Purpose of the Study:

  • To investigate the correlation between patent information and the rate of technological progress.
  • To identify specific patent metrics that best predict performance improvements.
  • To assess the predictive power and robustness of a combined patent metric for future technological advancement.

Main Methods:

  • Analysis of patent data within various technological domains.
  • Development and application of patent metrics focusing on importance, recency, and immediacy.
  • Statistical correlation and linear regression analyses to evaluate relationships with performance improvement rates.
  • Validation of findings across different technological domains and over extended future periods.

Main Results:

  • Patent importance, recency, and immediacy are all significantly correlated with technological progress.
  • A combined patent metric incorporating importance and immediacy shows a strong correlation (r = 0.76, p = 2.6*10(-6)) with performance improvement rates.
  • This metric demonstrates robustness across diverse technological domains and predictive power for over ten years.
  • Linear regression models using these metrics provide realistic estimates of technological domain performance improvement.

Conclusions:

  • Patent information is a strong predictor of technological progress.
  • A composite metric of patent importance and immediacy offers a reliable tool for forecasting technological advancement.
  • These findings have practical applications for estimating and guiding innovation trajectories in technological domains.