Intangible assets account for more than 70 per cent of the enterprise value of many companies, but these assets have historically been difficult to analyse en masse.
With the emergence of a Big Data solution, users are now given immediate access to business intelligence related to Intellectual Property, allowing them to make more informed decisions.
What is Intellectual Property?
Intellectual Property (IP) as an asset has been around for almost as long as land rights – the UK Land Registry was formed in 1852, and the UK Patent Office opened in 1862. Going further back, the first UK private land sale was in 1660, while the first patent was issued in 1449.
The term Intellectual Property covers a wide variety of intangible assets, many of which will be familiar – patents, trademarks, copyright, design rights, brands, and domain names are all examples of intellectual property. Most businesses are dependent on the existence of one or more of these assets.
In comparison to property, mortgages, futures and lending, Intellectual Property has not really evolved the same financial constructs, as it is somewhat harder to define in terms of scope and breadth. Many of these financial constructs do exist for some IP assets (typically patents), but they are unconventional and assessed individually at great cost and difficulty.
This is starting to change, and IP is evolving into a functioning asset class, but there’s still some way to go. Without an understanding of the value and risks associated with intellectual property rights it is difficult for this evolution to continue.
Why does it matter?
Again, intangible assets account for more than 70 per cent of the enterprise value of many companies, especially technology rich ones. However, these assets are generally not capable of being traded, exploited, valued, or compared.
Because intellectual property is a complex asset the value and associated risk are largely not understood by the people that make valuation or lending decisions.
Why Big Data?
A great deal of intellectual property data is available today, including patent, trademark and design data from the 200 IP registries around the world and litigation data from the courts.
Patent registries across the globe work more-or-less independently, so there is no one view of individual inventions protected in multiple countries, and no clear view of ownership. The information relating to ownership of IP assets are typically poorly normalised, with no consistent identifiers, and are often out of date. More than 1500 patent applications per day were sent to the US office alone in 2014, and any of them could have a profound effect on a sector, so tracking changes in this area can be critically important.
Identifying IP assets is only the starting point. To build up a complete picture requires the consideration of all data that impacts the value or risk of the assets – for instance ownership, licensing and litigation data. Once this data is assembled we can apply machine learning and data science to summarise, identify trends, spot anomalies, and find stable and reliable indications of risk and value.
A second challenge is then visualising those analytics, in a way they can be understood by non-specialist business people.
Analytics should be treated as the starting point for human interpretation, not a replacement for it, and by extension, the better the data and analytics, the better the basis on which human interpretation of the facts can be built.
A Big Data solution provides immediate access to business intelligence related to IP, leaving it for users to make informed decisions based on those same facts, circumstances, needs and requirements.
Knowing where your competitors are investing lets you counter future changes of direction, and knowing where your IP portfolio is strong or weak lets you know how you sit in the marketplace, and allows you to foresee future litigation risks.
Steve Harris, CTO of Aistemos