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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (11): 61-71    DOI: 10.11925/infotech.2096-3467.2022.0161
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Core Patent Portfolio Identification and Application in Professional Technical Field
Zeng Wen1,Wang Yuefen2,3()
1School of Intellectual Property, Nanjing University of Science and Technology, Nanjing 210094, China
2School of Management, Tianjin Normal University, Tianjin 300380, China
3Institute for Big Data Science, Tianjin Normal University, Tianjin 300380, China
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Abstract  

[Objective] This paper constructs identification methods for core patent portfolio and then examines their application with the help of large-scale datasets. [Methods] Through cross-combination, we constructed five identification models for the patents, which included six features of the patents. We then compared our methods’ performance with datasets of artificial intelligence. [Results] Different combined methods yielded highly consistent results when applied to various datasets. Meanwhile, as the number of core patents increased, the duplicated rates between the two methods gradually decreased. For example, the core patent duplication rates of method ① and method ④ dropped from 80% to 47%. [Limitations] We only investigated the common identification requirements. More research is needed to study those for specific and individualized areas. [Conclusions] The five constructed methods can be applied to different scenarios. For the rapidly developing field of artificial intelligence, the entropy weight method combining grey relational analysis and the entropy weight method with TOPSIS may yield better results.
Key words： Core Patent Identification      Combination Identification Methods      Single Weighting      Combination Weighting     
Received: 28 February 2022      Published: 13 January 2023
ZTFLH:  G306  
Fund:National Social Science Fund of China(16ZDA224)
Corresponding Authors: Wang Yuefen     E-mail: yuefen163@163.com
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Cite this article:

Zeng Wen,Wang Yuefen. Core Patent Portfolio Identification and Application in Professional Technical Field. Data Analysis and Knowledge Discovery, 2022, 6(11): 61-71.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0161     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I11/61

The Composition Design of the Core Patent Identification and Combination Method of Weighted Weighting and Calculation of Cross-Combination
指标 指标特征信息 特征信息的含义 反映的特点
技术价值 专利被引频次 专利被其他专利引用的次数 原创性及其影响力
引用专利数 专利文本引用他人专利的数量 创新程度与新颖性
引用科学文献数 专利文本引用科学文献的数量 科学关联程度
IPC小类数 专利包含IPC小类的数量 技术属性与覆盖范围
经济价值 同族专利数 同一发明思想向不同国家提出专利申请的数量 技术市场范围
权利要求数 专利文件中包含权利要求的数量 法定保护范围与经济性
Indexes of Core Patent Recognition and Selection of Characteristic Information
指标特征信息 被引频次 引用专利数 引用科学文献数 IPC小类数 同族专利数 权利要求数
最大值 757 1 057 1 776 15 149 1 442
最小值 0 0 0 0 0 0
算术平均数 0.675 6 0.700 1 3.487 8 2.023 9 1.698 3 11.226 7
标准差 5.737 1 8.546 3 23.227 3 1.276 8 2.576 6 13.076 3
Data Descriptive Statistics of Patent Feature Information
赋权方法 被引频次 引用专利数 引用科学文献数 IPC小类数 同族专利数 权利要求数
熵权法 0.300 2 0.222 8 0.380 7 0.020 6 0.044 2 0.031 5
TOPSIS 0.039 9 0.065 4 0.088 6 0.002 0 0.008 0 0.207 3
离差最大化法 0.146 6 0.200 8 0.147 3 0.109 6 0.111 8 0.283 9
AHP法 0.268 6 0.041 1 0.066 2 0.105 6 0.307 2 0.211 3
G1法 0.142 1 0.262 8 0.177 9 0.112 4 0.138 1 0.166 7
G2法 0.153 4 0.209 8 0.154 2 0.149 3 0.174 1 0.159 2
AHP与TOPSIS组合权重 0.167 6 0.265 3 0.123 9 0.023 4 0.080 5 0.339 3
多算法赋权组合方法权重 0.191 9 0.224 8 0.224 6 0.092 8 0.112 1 0.153 8
Calculation Results of Index Weights for Different Weighting Methods
方法① 方法② 方法③ 方法④ 方法⑤
方法① - 90% 20% 80% 80%
方法② 90% - 20% 80% 80%
方法③ 20% 20% - 30% 30%
方法④ 80% 80% 30% - 100%
方法⑤ 80% 80% 30% 100% -
Coincidence Rate Between Different Methods Based on Top10
方法① 方法② 方法③ 方法④ 方法⑤
方法① - 85% 42% 75% 80%
方法② 85% - 31% 85% 80%
方法③ 42% 31% - 41% 34%
方法④ 75% 85% 41% - 92%
方法⑤ 80% 80% 34% 92% -
Coincidence Rate Between Different Methods Based on Top100
方法① 方法② 方法③ 方法④ 方法⑤
方法① - 68% 23% 47% 59%
方法② 68% - 20% 55% 73%
方法③ 23% 20% - 35% 24%
方法④ 47% 55% 35% - 72%
方法⑤ 59% 73% 24% 72% -
Coincidence Rate Between Different Methods Based on Top500
方法 2011-2012 2013-2014 2015-2016 2017-2018 2019-2020
方法① 85 67 95 152 101
方法② 62 49 82 179 128
方法③ 81 53 62 128 176
方法④ 63 39 70 159 169
方法⑤ 70 44 78 171 137
整体重
复比		 70% 58% 76% 71% 63%
Time Distribution and Coincidence of Top500 Sets Based on Each Method
核心专利组合识别方法 赋权原理/算法 组合/排序方式 特点及应用
熵权法赋权结合灰色关联分析 熵权法计算权重凸显数据分散度大的指标信息 借助灰色关联分析寻找接近最优方案的评价对象 面向规模较大、数据较分散的专利数据集，识别出某一项或几项特征值表现突出的专利，尤其适合处于迅猛发展期的技术领域
熵权法赋权结合TOPSIS 熵权法计算权重凸显数据分散度大的指标信息 借助TOPSIS寻找接近最优方案、远离最劣方案的评价对象 面向规模较大、数据较分散的专利数据集，识别出某一项或者几项指标值表现突出且其他指标值均不趋近最低值的专利，尤其适合处于迅猛发展期的技术领域
AHP赋权结合TOPSIS 基于AHP法获取专家评价的具有关联程度的指标信息赋权 借助TOPSIS寻找接近最优方案、远离最劣方案的评价对象 面向数据时间跨度长，主观判断发挥作用，且专利价值被不断认可的评价应用情境，尤其适合处于发展成熟期的技术领域
AHP与TOPSIS组合赋权 AHP权重体现专家核心评价侧重，TOPSIS权重反映客观赋权情况 基于最小熵原理计算组合权重，并计算排序专利核心程度 面向需要综合主客观评价，且使两者赋权的各个特征信息权重差值处于最小化下识别核心专利的应用情境
多算法赋权组合方法 G1、G2、熵权、离差最大化分别计算权重 基于最大熵原理计算组合权重，并计算排序专利核心程度 面向需要综合主客观方法评价，且使组合赋权的各个特征信息处于综合表现均衡的核心专利识别的应用情境
Different Core Patent Identification Methods and Their Applications
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