Detecting Strategic Patent Filing Anomalies: Early Warning Signals for Market Disruption

Building on Tjandrawinata & Budi (2025) who emphasize structured innovation systems for patent success, and Hinsley et al. (2024) who use machine learning to spot wildlife-trade trends in patents, this idea proposes developing algorithms to systematically detect “anomalous” patent filing behaviors—such as sudden bursts by unlikely actors, cross-sector filings, or unusual international filings. Unlike traditional trend analyses (e.g., Afkar et al., 2024; Jayaraman & Prakash, 2024), this approach doesn’t just map trends but actively hunts for deviations from expected filing behaviors within and across industries. By correlating these anomalies with subsequent market shifts (e.g., new entrants, product category disruptions), the research could offer a novel early-warning system for incumbent firms and regulators. This is particularly innovative as it shifts the focus from descriptive analytics to predictive, anticipatory IP management. The impact? It could radically improve strategic foresight and competitive intelligence in fast-moving sectors.

References:

1. The Structured Innovation System: Optimizing Patent Filing and Obtaining Global Protection. R. Tjandrawinata, Henry Soelistyo Budi (2025). Journal Of World Science.
2. Early warning of trends in commercial wildlife trade through novel machine-learning analysis of patent filing. A. Hinsley, D. Challender, S. Masters, D. Macdonald, E. J. Milner-Gulland, J. Fraser, J. Wright (2024). Nature Communications.
3. An Innovation Analysis of Smart Grid Patent Landscape in the Industrial Sector. K. Afkar, Untung Usada, Taqwanur, Luqman Hakim, Agus Rachmad Purnama (2024). 2024 7th International Conference on Green Technology and Sustainable Development (GTSD).
4. Strategic Patent Portfolio Management in the Sodium‐Ion Battery Industry: Navigating Innovation and Competition. V. Jayaraman, A. S. Prakash (2024). Energy Storage.