Standard-essential Patent Prediction: Discussion of Patent Standardization Time

Standard-essential patents are crucial tools in international trade competition, and standard-essential patent prediction is becoming competition point among countries. However, existing standard-essential patent prediction methods cannot adequately use various patent features, and cannot provide verifiable predicted results on whether a valid patent can be a standard-essential patent or not. To address these issues, this paper proposes a dynamic standard-essential patent prediction model. In our model, we partition different time slots for each patent to extract the dynamic features in each time slot of the patent, and the static features of the patent are extracted. Then we dynamically predict whether the patent can be a standard-essential patent in the next few years or not. The experimental results show that our model significantly outperforms the baseline models in the values of accuracy, precision, recall and F1.

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Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10890086
Citations
1
Primary Topic
Intellectual Property and Patents
Type
article
Field-Weighted Citation Impact
2.76

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Standard-essential Patent Prediction: Discussion of Patent Standardization Time

Yan Cao, Keqin Gan, Zonghan Bai, Weidong Liu
1 citations
Intellectual Property and Patents
2.76
article

Standard-essential Patent Prediction: Discussion of Patent Standardization Time

Yan Cao, Keqin Gan, Zonghan Bai, Weidong Liu
article en
1 citations

Abstract

Standard-essential patents are crucial tools in international trade competition, and standard-essential patent prediction is becoming competition point among countries. However, existing standard-essential patent prediction methods cannot adequately use various patent features, and cannot provide verifiable predicted results on whether a valid patent can be a standard-essential patent or not. To address these issues, this paper proposes a dynamic standard-essential patent prediction model. In our model, we partition different time slots for each patent to extract the dynamic features in each time slot of the patent, and the static features of the patent are extracted. Then we dynamically predict whether the patent can be a standard-essential patent in the next few years or not. The experimental results show that our model significantly outperforms the baseline models in the values of accuracy, precision, recall and F1.

Inner Mongolia University (CN), Institute of Scientific and Technical Information of China (CN), China National Institute of Standardization (CN)
National Science Foundation, China Postdoctoral Science Foundation, Science and Technology Development Fund
Openalex Percentile: Top 13%
Intellectual Property and Patents
2.76
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