Innovation serves as the fundamental driver of economic development. As basic units of economic society, the innovation investment of individual enterprises not only forms the cornerstone of long-term corporate development but also plays a crucial role in shaping the global innovation landscape. A direct manifestation of corporate R&D innovation capabilities lies in both patent quantity and quality. However, patent quality cannot be simplistically measured, as evaluation criteria include citation frequency, grant rate, technological scope coverage, and number of claims.
Patent citation refers to subsequent patent applications citing prior patents, indicating technical correlations between inventions. Originating from the Science Citation Index (SCI), this concept establishes knowledge networks through academic literature citations. Around February 1947, the USPTO (United States Patent and Trademark Office) first attempted to list relevant references on granted patent documents for evaluating patent claims. Currently, patent citation information primarily derives from two sources:
- 1. References provided by inventors during application, typically cited in the "Background Art" section of patent specifications to differentiate prior art and demonstrate novelty. For instance, the U.S. patent system mandates applicants to disclose all relevant technical materials through Information Disclosure Statements (IDS) during the entire application process; otherwise, patents may be invalidated.
- 2. References added by patent examiners during review. Examiners conduct prior art searches to assess patent novelty and non-obviousness within the relevant technological domain.
Patent citation data serves at least two critical functions:
- Tracking technological evolution and knowledge flow. Since Narin (1994) introduced bibliometrics into patent analysis, patent citations have been recognized as objective indicators of knowledge linkages. Citation networks reveal dynamic innovation processes and cross-sectoral knowledge transfer patterns.
- Measuring innovation quality and value. Innovation evaluation requires moving beyond quantity metrics, as patent significance varies substantially. Citation frequency provides critical insights into patent quality and technological value.
The seminal work Patents, Citations and Innovations by Adam Jaffe and Manuel Trajtenberg demonstrates how patent citations can analyze technological value and innovation trends.
CnOpenData has systematically organized U.S. Patent Information Data into specialized databases, providing comprehensive research support for academic investigations.
Data Characteristics
- Contains detailed information on both citations made by patents and citations received by patents
- Includes current legal status tracking for each patent
Database Usage Guide
Reprint: The use and misuse of patent data: Issues for finance and beyond
Time Coverage
1985-12-31 to 2024-12-31
Field Specifications
Sample Data
Basic Information of U.S. Patents
U.S. Patent Citation Information
U.S. Patent Cited Information
U.S. Patent Transaction Records
Literature Cited by U.S. Patents
U.S. Patent Classification Codes
Relevant Literature
- Deepak Hegde, Alexander Ljungqvist and Manav Raj, 2022, "Quick or Broad Patents? Evidence from U.S. Startups", The Review of Financial Studies.
- Murat Alp Celik, Xu Tian and Wenyu Wang, 2022, "Acquiring Innovation under Information Frictions", The Review of Financial Studies.
- Tao Shu, Xuan Tian, and Xintong Zhan, 2022, "Patent quality, firm value, and investor underreaction: Evidence from patent examiner busyness", Journal of Financial Economics.
- Valentin Haddad, Paul Ho, Erik Loualiche, 2022, "Bubbles and the value of innovation", Journal of Financial Economics.
Update Frequency
Annual updates