The Right Information to Display in a Patent Search – Can IP Intelligence Help?

Published on 21 Apr, 2016

IP Intelligence

As a growing number of countries see the gains in establishing and empowering systems to safeguard intellectual property, global patent databases are likely to witness an exponential growth in the data they’re burgeoned with.

In a previous post, we delved into the impact of Big Data analytics on methods and processes to conduct a search of existing intellectual property. In this post, we’ll examine why it’d be good to draw on other allied databases in tandem in order to derive more meaningful insights.

We’re grappling with a deluge of patent databases today, insightful stores where one can execute complex patent search queries that address specific parameters or needs. The most common process of performing a search is:

  • Prepare a Query — based on a particular concept
  • Run the Query — through the available search interface
  • Analyze the Results — which usually consist of patent sets that your query pulls up

The returned results will have plenty of information about the patents you’ll need to evaluate, mostly bibliographic details that help in analyzing your results. Databases usually provide convenient options to customize how these details are displayed and sorted.

Most patent researchers resort to the basics such as Title, Abstract, Assignee/Inventor, and so on. More often than not though, these details fall far short of being useful while handling our assignments.

As patent databases mushroom worldwide — with constant additions to their fields and features — handling Big Data will need to be an integral part of IP analytics.

With advanced analytics tools that provide easy-to-understand insights within the web of patent connections, Big Data analytics can be brought into play here to display a host of other useful information.

A database equipped with Big Data analytics can make intelligent decisions about the congruence between patents/results and display intelligent information. Let’s consider a case where you’re looking for prior art related to patent X. As per current database capabilities, we can sort the query’s results (say Y patents) as per relevancy, which is done by mapping of the search terms with patent keywords. Existing Big Data features can scour through additional data for Y’s patents and display other useful information. For example, if any of Y’s patents have been used for re-examination/litigation against a patent similar to X, that particular Y patent can be highlighted.

One can also sort the result set of Y patents according to their usage in common litigation/re-examination cases. This can help club similar patents for analysis in a more efficient manner. We would need to link up re-examination/litigation databases for this however, although it wouldn’t be really difficult to manage.

Similarly, you could also hook up databases for non-patent literature in order to draw additional information around a patent. It could, for instance, throw up something like a research paper by the patent’s inventor for a similar concept.

Envisage a brilliant interface for IP analysts wherein we’ve moved on from archaic views of bibliographic details – all thanks to Big Data analytics in intellectual property.

The possibilities are indeed endless, and there’s a lot more to come.