Patents are proprietary inventions that are registered with governmental and intergovernmental bodies. These inventions may be machines, manufacturing processes, compositions, improvements on earlier patents—the only requirements are that they be new, useful, and not obvious.
Patent data is the collection and analysis of information about patents.
This data is publicly available, though difficult to comb through for several reasons. First, not all inventors choose to or are able to apply for patents with more than one country or with international bodies. Second, those who do may not register their trademarks simultaneously, leading to a year or nearly two years delay in registering patents at the least. Thirdly, the sheer volume of patent data, even restricted to just one country, can be overwhelming.
Aside from national patent offices, there are international bodies with public databases to search through (though they may require you sign up for an account at their site, as the WIPO IP Portal does). Freely accessible patent databases that do not require you to sign up for accounts include the WIPO PATENTSCOPE database and the WIPO DAS (Digital Access Service), and the EPO’s Espacenet.
Private companies also provide patent data, often with services like analytics or browser add-ons that provide patent information for companies whose professional pages you browse, as IPQwery offers.
Common attributes of patent data include inventor, nation, publication number, publication data, invention title or description or abstract. Databases should also have historical information on patents, such as changes in patent ownership. Additionally, some databases record the inventor’s sex, in order to determine whether a country or region offers gender parity in inventions.
This data is primarily used for reference and research. Across all industries and organizational levels, patent data provides key information.
For example, individual inventors or development teams use this data to determine whether there is even a need to continue in their development efforts. On the other hand, developers can use patent data as inspiration for their own efforts or as sources of likely partners in research and development. Managers and other decision-makers can use the data to conduct competitor or market research on industries or nations. Finally, policy-makers use this data to propose improvements in economic activity or education.
This data has already been reviewed by potentially hundreds of people before being made available in databases. Therefore, there is little need for quality control of the data itself.
Where problems tend to occur is in the search process and the analysis of the data itself. Even if individuals perform their searches properly in the early research and development stages, they will not discover all patents.
Additionally, patent titles do not always give a complete picture of the invention itself. Any dataset analysis should cover the entire text of the patent. NLP models are very helpful here.
If you choose to use a data vendor like one of the ones in our site, consider their reputation and request a sample dataset to check for completeness, consistency, timeliness, and relevancy of the data to your needs.
“Patents are widely used as an indicator of how much innovation is taking place, where and in which fields. Taking a deeper look at the data can therefore provide a range of insights into innovation in [the renewable energy] sector.”
RoyaltyStat’s dataset – ‘Global Royalty Rates from Intellectual Property License Agreements’ provides Legal and IP Data, Patent Data and Economic Data that can be used in
Quant IP’s dataset – ‘Quant IP Patent and Innovation Metrics Global – complete package for all companies with patent activity’ provides Economic Data, Company Data and Patent Data that can be used in Portfolio Management and
Quant IP’s dataset – ‘Quant IP Patent and Innovation Metrics for 400k companies, 25+ years history’ provides Economic Data, Company Data and Patent Data that can be used in