Ambercite is the owner and developer of the patent analysis tool known as Network Patent Analysis (NPA). NPA™ patent analysis harness the collective intelligence of patent applicants to provide unique insight on the leading patents, sub-technologies and patents owners in any business area. NPA™ is currently available as a service, and is undergoing constant development to provide a more powerful insight.  We welcome any enquires you may have as to how NPA patent analysis can improve your understanding of your technology and business landscape.

Why patent mapping?

More than 1.9 million patents are added to the more than 60 million published patents every year. Patent data is increasingly recognised as the leading source of information on technology developments. Patents are filed by a broad range of innovators, including large and small companies, universities and research institutes, individual inventors and patent investors. Even the most secretive of companies may disclose many key details of their innovation program through their patent filings. 

Why Network Patent Analysis? (NPA)

Every decision made by a patent applicant to file a patent is a 'vote' for the technology being patented. Patent citations suggest which of these patents should be grouped together. NPA™ patent analysis applies sophisticated algorithms to what can be over one hundred thousand patent applications and up to one million patent citations to group and rank patents in a statistically robust manner. Users of NPA™ patent analysis benefit from the collective intelligence of patent applicants and examiners. Unlike other patent mapping methods, NPA™ patent analysis is capable of objectively distinguishing very similar patented technologies, including distinguishing patents drafted using identical technical keywords.

Network Patent Analysis produces both visually insightful graphics and data in spreadsheet form suitable for further analysis. Ambercite can arrange further analysis of NPA data in a variety of ways, or support clients to analyse their own data.