How to adapt your IP strategy to machine learning evolution
The rise of machine learning has profound implications for most areas of business, and intellectual property is no exception.
What once seemed the stuff of science fiction – the idea that machines would be able to do many things not just faster but actually better than humans – is now a reality, and a good IP strategy over the next few years will be one that takes account of this transformation.
The good news is that artificially intelligent machines should make various aspects of IP more efficient. For example, using artificially intelligent machines to conduct trademark searches should save time and improve accuracy. Perhaps the biggest single advantage of machine learning is the ability to quickly and accurately analyse large datasets, and then to retain the knowledge acquired to inform future IP strategy.
However, the implications for trademark holders are not limited to more efficient search. In practice, the ability of machine learning to tackle large amounts of data effectively could make the whole process of launching brands far more effective. Consider how much your efforts could be enhanced by a machine learning solution that not only helped with trademark clearance, but was also able to provide better information on where to file a trademark, or on which points of your existing portfolio are most vulnerable and need boosting.
Many different considerations will affect an IP strategy, but all companies are likely to have a filing strategy and a renewals strategy. Machine learning has implications not just for new filings, but is also able to improve your renewals strategy.
By analysing and learning from large amounts of data, specifically how different assets have performed in different scenarios historically, AI will be able to help inform your judgment about which trademarks are worth renewing and which you can let lapse. This is likely to save you money, but also the resources associated with having a baggy portfolio.
The most likely cause of failure in any IP scenario is human error. No matter how good your internal processes are, IP administration can always be victim to an incorrect keystroke or a simple oversight. Unfortunately, such errors can often have negative effects far out of proportion to the gravity of the initial error. At worst, they can result in the loss of a valuable IP asset.
Machine learning mitigates this risk considerably, since as long as the information the machine has at the start of the process is accurate, in theory the rate of ‘avoidable error’ should be close to zero.
It’s not just companies with large trademark portfolios whose IP strategy will benefit from machine learning. Patent holders may benefit even more from this new technology. Prior art search, for example, is an area ripe for transformation by artificial intelligence, which can search quickly and effectively across multiple databases for relevant prior art, potentially saving hundreds of man hours in the process.
And in high-technology industries in particular, the ability to quickly analyse the competitive filing landscape and map it onto your existing portfolio will help you identify new filing opportunities and expose potential weaknesses in your portfolio far quicker than is currently possible.
Machine learning is already helping companies with their IP strategy, notably by freeing up resources to enable people to do what they do best: make decisions.
But the technology is still in its infancy, and the number of potential applications for it have only just begun to be explored. It’s possible, even likely, that in due course machine learning will enable technology to generate, file and administer intellectual property assets. That could involve analysing patent office practice to enable smarter drafting, managing and executing translations for patent applications, or deciding on and even executing trademark renewals in a timely and effective fashion.
The possibilities are endless, and companies that embrace those opportunities quickly will be the ones to derive the greatest competitive benefit from them.
So what can you do? Well, a good place to start is to understand how your business can benefit from machine learning. Then take steps to actually use the available solutions, whether in-house or through a third party. But whatever you do, the time to start is now!