According to statistics from the Ministry of Commerce (MOFCOM), China has ranked first globally in invention patent applications for eight consecutive years as of 2018. However, a high volume of patents does not inherently indicate high quality. Just as citation counts serve as a crucial metric for evaluating academic paper quality, patent citation counts represent a core indicator of patent quality.
Patent citation refers to the referencing of a patent by subsequent patent applicants or examiners, signifying technical relevance between two patents. Originating from the Science Citation Index (SCI), patent citations form a knowledge network analogous to scholarly literature references. Around February 1947, the USPTO (United States Patent and Trademark Office) pioneered the practice of listing relevant prior art references on granted patent documents to evaluate patentability. Currently, patent citation information generally derives from two sources:
- References provided by inventors during the application process, typically cited in sections like "Background Technology" within patent specifications. These references elucidate distinctions between the invention and prior art while demonstrating novelty. For instance, the U.S. patent system mandates applicants to disclose all known relevant technical materials via an Information Disclosure Statement (IDS) throughout the application process; failure to do so may result in rejection or post-grant invalidation.
- References added by patent examiners during substantive examination. To assess novelty and non-obviousness, examiners conduct prior art searches to identify existing technologies closest to the claimed invention’s technical field.
Patent citation data serves at least two primary functions:
- Tracing technological trajectories and knowledge flows. Since Narin (1994) introduced bibliometrics into patent analysis, patent citations have been recognized as objective indicators of knowledge linkages. If a patent cites earlier patents, it implies the utilization of knowledge embedded in those prior inventions. Citation networks reveal dynamic innovation processes and characterize knowledge flows across sectors and industries.
- Assessing innovation quality and value. Measuring innovation extends beyond quantity to encompass quality, as patents exhibit significant heterogeneity in importance and value. Raw patent counts fail to capture the full innovation landscape, whereas citations help evaluate patent quality and innovative significance.
Adam Jaffe and Manuel Trajtenberg’s seminal work Patents, Citations, and Innovations establishes frameworks for analyzing patent value and technological trends through citation relationships.
CnOpenData highlights distinctive features of this dataset in the "Data Features" section below.
Data Features
- Includes citations to Chinese patents not only by domestic applicants but also by patents filed worldwide.
- Provides detailed information on both patents cited by the focal patent (forward citations) and patents citing the focal patent (backward citations).
- Documents the current legal status of each patent across jurisdictions.
- Identifies whether citations were added by examiners during prosecution.
Publications Citing This Dataset
- Yu Zhen, Li Jinpo, Jiang Shengjun, 2024: "Spatial Agglomeration of High-Level Talent and Innovation in Latecomer Nations: Evidence from Chinese Individual Patent Data", Economic Research Journal 8.
- Peng Yuanhuai, 2023: "Value Creation Through Government Data Openness: A Total Factor Productivity Perspective", The Journal of Quantitative & Technical Economics 9.
- Shang Yuping, Pan Zhou, Meng Meixia, 2023: "Innovation Performance of Polycentric Spatial Strategies in Chinese Cities: Perspectives of Agglomeration Economies and Amenities", China Economic Quarterly 3.
Database Application Guide
Reposted: What Research Can Be Conducted Using Patent Citation Data?
Reposted: Understanding the Relationship Between Patent Citation Counts and Patent Value
Reposted: Mining High-Value Patents in Chinese Universities: Big Data Analysis of Inventor Citations
Reposted: Proper Use, Misuse, and Abuse of Patent Data in Finance, Accounting, and Economics Research
Reposted: Chinese Patent Numbering System
Time Coverage
- Invention applications: Statistics based on publication date (1985–2024)
- Granted inventions/utility models/designs: Statistics based on grant announcement date (1985–2024)
Field Descriptions
Sample Data
Basic Information of Chinese Patents
Citation Received by Chinese Patents
Citations Made by Chinese Patents
Legal Proceedings of Chinese Patents
References Cited by Chinese Patents
Classification Codes of Chinese Patents
Relevant Literature
- Liu Xiuyan, Wang Qiao, 2022: "Boundary Effects of Knowledge Spillovers: Evidence from Patent Citation Data", Economic Research Journal 11.
- Zhao Ziye, Yang Qing, Chen Jianbo, 2018: "Generalist vs. Specialist: CEO Ability Structure and Corporate Innovation", Management World 2.
- Jan Bena, Hernán Ortiz-Molina, Elena Simintzi, 2022, “Shielding firm value: Employment protection and process innovation”, Journal of Financial Economics.
- Moser, P., J. Ohmstedt and P. W. Rhode, 2018, “Patent Citations—an Analysis of Quality Differences and Citing Practices in Hybrid Corn”, Management Science.
- Jaffe, A. B., M. Trajtenberg and R. Henderson, 1993, “Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations”, The Quarterly Journal of Economics.
- Roach, M. and W. M. Cohen, 2013, “Lens or Prism? Patent Citations as a Measure of Knowledge Flows from Public Research”, Management Science.
- Josh Lerner, Amit Seru, 2021, "The Use and Misuse of Patent Data: Issues for Finance and Beyond", The Review of Financial Studies.
Data Update Frequency
Annual updates