A dynamic model for predicting standard-essential patents

A dynamic model for predicting standard-essential patents

Journal article

1 volume.

With the rapid growth of intellectual property and standardization, standard-essential patents (SEPs) are gaining global attention. Accurately and swiftly predicting SEPs is becoming a key indicator of a country’s international influence. However, current research faces challenges: existing methods underutilize diverse patent features, cannot predict patent standardization time, and lack suitable datasets for dynamic SEP prediction. To address these issues, this paper proposes a dynamic SEP prediction model. Given a patent, we first extract its static features, then divide multiple time slots of varying lengths to capture dynamic features. We employ multiple Transformer models to dynamically predict whether the patent will become a SEP in the coming years. Additionally, we construct new datasets specifically for patent standardization time prediction. Comparative experiments show our model significantly outperforms baselines. Ablation studies further reveal the impact of different patent feature types on dynamic SEP prediction.

Crossref

World Patent Information ; 86, September, 2026

World Patent Information

Oxford [England] : Elsevier Ltd., 2026.

Anglais

https://www.sciencedirect.com/science/article/abs/pii/S0172219023000108

External Online Journals > World Patent Information