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This study introduces a quantitative method to predict when and where innovations will be patented. Using "context similarity" between technological codes, it forecasts future patenting activity and identifies leading countries.

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Area of Science:

  • Innovation studies
  • Quantitative analysis of technological progress
  • Patent analysis

Background:

  • Previous work established testable predictions of innovation events using pairs of technological codes.
  • The vector space of codes and "context similarity" metric enable quantitative analysis of technological progress.

Purpose of the Study:

  • To propose a quantitative approach for predicting the timing and location of patenting activity.
  • To extend the application of "context similarity" for forecasting innovation events.

Main Methods:

  • Utilizing a vector space model of technological codes.
  • Applying the "context similarity" metric to analyze relationships between codes.
  • Developing a scoring system for countries based on patenting probability.

Main Results:

  • The "context similarity" metric can predict the likelihood of future patenting activity.
  • This approach allows for forecasting the geographical location of upcoming patent filings.
  • Countries can be scored on their probability of being the first to patent new innovations.

Conclusions:

  • The developed quantitative approach effectively predicts both the timing and location of patenting activity.
  • "Context similarity" offers a powerful tool for understanding and forecasting technological innovation.
  • This method advances the field of innovation studies by providing predictive capabilities.