Anticipating Innovation Using Large Language Models

📰 ArXiv cs.AI

Use Large Language Models to anticipate innovation by analyzing patent language, allowing for predictive signals decades in advance

advanced Published 7 May 2026
Action Steps
  1. Collect patent data using APIs or web scraping
  2. Preprocess patent text using tokenization and normalization
  3. Train a Large Language Model on the patent corpus to identify patterns
  4. Apply predictive modeling to detect early signals of forthcoming combinations
  5. Validate results using historical data and expert evaluation
Who Needs to Know This

Data scientists and researchers can benefit from this approach to forecast emerging technological combinations, informing policy and investment decisions

Key Insight

💡 Collective language shifts in patents can predict innovation decades in advance

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🚀 Anticipate innovation with LLMs! Analyze patent language to forecast emerging tech combinations 📈

Key Takeaways

Use Large Language Models to anticipate innovation by analyzing patent language, allowing for predictive signals decades in advance

Full Article

Title: Anticipating Innovation Using Large Language Models

Abstract:
arXiv:2605.04875v1 Announce Type: cross Abstract: Forecasting innovation, intended as the emergence of new technological combinations, is a fundamental challenge for science and policy. We show that forthcoming combinations leave an early trace in the collective language of patents, with predictive signals detectable even decades in advance. We show that signal is not attributable to any single inventor, but emerges as a collective shift in how technologies are described across thousands of pate
Read full paper → ← Back to Reads

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