Patent license prediction using deep survival analysis: A comparative study

We evaluate deep survival analysis frameworks for patent licensing prediction, systematically comparing cure rate models against standard approaches and quantifying the benefit of temporal modeling over static classification to address unresolved methodological questions following recent applications of neural survival models to patent contexts.

We conducted experimental validation on 624,129 United States Patent and Trademark Office (USPTO) patents (2016-2017), comparing classical Cox regression, deep neural network baseline, DeepSurv (neural Cox proportional hazards), and Cox cure rate models. Performance was evaluated using Recall@K metrics across 1-year, 3-year, and 5-year prediction horizons, with statistical significance assessed through patent-level bootstrap resampling (B = 1,000).

All deep learning approaches achieve high performance (Recall@10% 89.8-91.9%), substantially outperforming classical Cox regression (70.6-72.3%). Among neural architectures, DeepSurv-a standard neural Cox proportional hazards model-offers the optimal balance of simplicity and performance. More complex cure rate extensions provided no clear additional benefit over standard DeepSurv within the scope of this study: although bootstrap resampling detected a small but statistically significant difference (0.70 percentage points), this gap is practically negligible for patent screening, suggesting that explicit population heterogeneity modeling offers limited practical gains when deep feature learning is employed. Survival modeling provides statistically detectable but modest gains over static binary classification (Recall@10% differences of 0.5-1.6 percentage points), indicating that the primary value stems from capturing complex patent characteristics rather than explicit temporal modeling.

This study provides, to the best of our knowledge, the first systematic evaluation of cure rate models and temporal modeling benefits for patent licensing prediction, demonstrating that architectural simplicity (DeepSurv) achieves near-optimal performance (Recall@10% 91%) when deep feature learning is prioritized. The findings offer actionable guidance for patent portfolio management: invest in feature learning infrastructure rather than architectural sophistication.