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2025 , Vol. 69 >Issue 16: 79 - 89

DOI: https://doi.org/10.13266/j.issn.0252-3116.2025.16.007

研究论文

基于Catboost模型的专利可交易性预测研究——来自美国2002—2012年间物理类专利数据的实证

  • 丁群哲 ,
  • 宋永辉 ,
  • 宋鸿杰 ,
  • 冉从敬
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  • 1 武汉大学信息管理学院 武汉 430072;
    2 武汉大学信息资源研究中心 武汉 430072
丁群哲，博士研究生;宋永辉，博士研究生，通信作者，E-mail:yonghuisong@whu.edu.cn;宋鸿杰，硕士研究生;冉从敬，教授，博士，博士生导师。

收稿日期: 2024-09-09

  修回日期: 2025-02-13

  网络出版日期: 2025-08-01

基金资助

本文系国家自然科学基金面上项目“基于图卷积神经网络的新兴技术领域高质量专利识别及其演化研究”（项目编号： 72274084）研究成果之一。

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Predicting Patent Tradability Using Cat Boost: An Empirical Study Based on U.S. Physics Patents from 2002 to 2012

  • Ding Qunzhe ,
  • Song Yonghui ,
  • Song Hongjie ,
  • Ran Congjing
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  • 1 School of Information Management, Wuhan University, Wuhan 430072;
    2 Center for Studies of Information Resources, Wuhan University, Wuhan 430072
Ding Qunzhe, doctoral candidiate; Song Yonghui, doctoral candidiate, corresponding author, E-mail: yonghuisong@whu.edu.cn.;Song Hongjie, master’s candidiate; Ran Congjing, professor, PhD, doctoral supervisor.

Received date: 2024-09-09

  Revised date: 2025-02-13

  Online published: 2025-08-01

Supported by

This work is supported by the general project of National Natural Science Foundation of China, titled“High-Quality Patent Identification and Evolution Analysis in Emerging Technology Fields Using Graph Convolutional Neural Networks” (Grant No. 72274084).

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摘要

[目的/意义] 识别与预测专利交易潜力对落实科技强国战略、推动科技成果转化、促进产研协同创新具有重要意义。[方法/过程] 将专利视为技术单元，根据以往研究成果筛选出可交易专利，并基于Incopat和壹专利数据库，对专利的交易及其相关的技术特征进行统计学测度分析。在此基础上，提出融合多维度专利特征的可交易专利的预测方法。将可交易专利预测问题转化为监督式二分类任务，给定专利包含法律、技术和经济的多维特征，以其在失效前是否发生交易行为作为预测目标，训练机器学习模型。[结果/结论] 研究结果表明，可交易性较高的专利存在专利权人数量较少、引用数量和权利要求数量较多等特点；优先申请间隔是专利可交易特征中最具影响的指标，较长的优先申请间隔帮助降低专利交易的可能性；CatBoost(Categorical Boosting)模型在综合性能与训练效率上取得相对较好的表现，是预测专利可交易性的最佳模型。然而,专利交易的预测仍是一个困难的任务，后续研究可尝试通过增加专利文本和图像特征等角度进一步提升性能表现。

关键词： 专利分析; 专利交易; 预测研究; 机器学习

本文引用格式

丁群哲 , 宋永辉 , 宋鸿杰 , 冉从敬 . 基于Catboost模型的专利可交易性预测研究——来自美国2002—2012年间物理类专利数据的实证[J]. 图书情报工作, 2025 , 69(16) : 79 -89 . DOI: 10.13266/j.issn.0252-3116.2025.16.007

Abstract

[Purpose/Significance] Identifying and predicting patent tradability is vital for implementing the innovation-driven strategy, accelerating technology commercialization, and promoting industry–research collaboration. [Method/Process] This paper treated patents as technological units and screened tradable ones based on prior research. Using the Incopat and YiPatent databases, patent transactions and their related technical characteristics were analyzed through statistical methods. Based on this, a prediction model for tradable patents was proposed, incorporating multidimensional features. The task was framed as a supervised binary classification, utilizing the legal, technical, and economic features of patents to predict the likelihood of a transaction before expiration, with machine learning models trained accordingly. [Result/Conclusion] The results are as follows. Patents with higher tradability are characterized by fewer patent holders, a greater number of citations, and more claims. The priority filling gap is the most significant indicator of patent tradability, with longer gaps reducing the likelihood of transactions. The CatBoost（Categorical Boosting） model demonstrates strong overall performance and training efficiency, making it the most effective model for predicting patent tradability. Nonetheless, predicting patent transactions remains challenging, and future research could improve performance by incorporating patent text and image features.

Key words： patent analysis; patent transactions; predictive research; machine learning

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