Harnessing Opportunities of AI to Decode Innovation and Patents

Innovation is the driver of progress and success in business.

Satya Nadella, Executive chairman and CEO of Microsoft

If innovation is the key to success, and intellectual property (IP) is the primary way innovation is protected, then why do research and development (R&D) teams generally have so little regard for IP? The preferred answer to this question is lack of understanding.

IP, and specifically patents, is complex. This complexity exists on many levels. This starts with patent law, its constant evolution and global harmonization, which sits on tectonic plates that rarely ever align. Then there is the patent lifecycle, which can take over 5 years from application to grant, before the licensing and litigation twists and turns that can continue for decades. But more than anything, it’s a people problem. R&D teams simply do not understand why patents matter. As with all sweeping generalizations, there are notable exceptions, such as the teams working in biotech and pharma. But even here, there are stories that would make your toes curl. In a recent conversation with an IP team within an mRNA vaccine leader, a budget request for patent analytics was rejected by a finance team – in a market for COVID-19 vaccines worth more than $100B.

Why patents matter

Conventional wisdom states that intangible assets account for over 80% of all enterprise value, and a large part of that is IP. Hence, investment in IP is critical. But no one responds to aphorisms.

Take an extreme such as Red Hat, whose business is open-source software (OSS); ensuring that patents are not used to impede OSS innovation is imperative. Red Hat engineers are passionate about openness. So why does Red Hat own more than 3,500 patents? The answer lies in the clarity of communication. Patents for Red Hat help protect the OSS community. Red Hat helped create Open Invention Network (OIN) and LOT Network, organizations that use innovative patent structures to mitigate risk. OIN uses cross-undertakings, and LOT created the license for the transfer. While the legal constructs might be complicated, OIN and LOT have been hugely successful because the message they communicated to business leaders was clear – use your patents to mitigate risk. Leadership teams understand the management of risk.

The same clarity is required when value creation is the primary strategy. Using Standard Essential Patents (SEPs) as an example, billions are invested in developing telecommunications standards, enabling a market worth trillions. Yet there remains epic amounts of controversy about whether more rights should be given to SEP owners (licensors) or more freedoms to those who implement (licensees). In this situation, lobbyists construct many conflicting legal and economic arguments. The extremes are the end of investment in standards (lower royalties mean less incentive to innovate) and the absence of new product implementations (higher royalties mean lower margins). While achieving a fair and reasonable balance is hard, at least there is the clearest connection between patents and value.

However, patent owners are often unable to articulate why patents matter. As a result, patent teams face increased scrutiny and budget pressure.

How harnessing AI helps with education and communication

Patent information is the largest library of scientific information in the world. This qualifies the data as an essential source of insight. Consider this range of questions:

• New products and features: is someone else already working on this?
• Protectability: can we stop others from copying our product?
• Alternatives: what other solutions exist to the problem in hand?
• Build or Buy: Should we go with it alone or collaborate with others?
• Trendspotting: is the market moving towards or away from a specific technology?

Go back 20 years, and it was all but impossible to find answers to these questions in patent data. Not because the information didn’t exist (patents have always been published) but because there was no cost-effective way of searching and analyzing the data. It was only 10 years ago that Cipher (now part of LexisNexis IP Solutions) launched a platform that could cluster and classify global patent data without any knowledge of Boolean strings or CPC/IPC codes developed by patent offices. Answering any basic questions listed above could easily be weeks of effort and/or cost tens of thousands of dollars. The business reality is that if you make something hard, slow or expensive enough, people stop asking. AI is a game changer. We are now at the point where patent information is accessible, like routine business information. Simply enter your question into a box and get real-time results. Below is the home screen of LexisNexis TechDiscovery, the most recently launched AI search

Creating a landscape view with just a few words

Its power is in its simplicity. It responds to one input only – curiosity. The need to know what’s going on. How you interact is your choice:

• Natural language – simply enter a text string.
• Science – use a paragraph from an academic paper.
• Patent – enter a patent number, either one owned by your company or someone else’s.

The platform delivers a set of patents from across the defined landscape in no time. It’s impressive technology, but is it AI? The better question is, perhaps, does it matter? For the technically curious, it includes many AI techniques developed by Cipher and is now enhanced with the assistance of an Anthropic large language model (LLM).

What does matter is that patent teams contribute to innovation. As suggested by Nadella at Microsoft, that will connect patents to commercial success. The historic problem with patent information has been its complexity, combined with the time and cost involved in preparing reports. The next wave of patent analytics will be defined by ease of use, which will improve communication between teams with different perspectives but similar innovation goals. It’s not simply AI magic that is bridging the gap between IP and R&D, it’s something much more human – communication. Today’s analytics have levelled the playing field such that more time can be devoted to the impact of the data and less time on the process of creation and curation.

Harnessing AI: Patent teams are open for business

It is universally accepted that IP teams are having to do more with less, and this reality is likely to continue for some time yet. While the temptation might be to reduce the amount of interaction between the patent teams and R&D, there is a compelling reason to do the opposite.

If patent teams provide solutions that deliver value or mitigate risk situations, this will change the narrative. It also unlocks the potential to combine patent data with other sources of business information, such as financials. The sooner that patent information stops being a specialist silo the better. Patent data is one of most valuable datasets in the world today and unappreciated. The speed, accuracy and ease of use enabled by AI analytics solutions will democratise this dataset. All IP professionals are encouraged to engage so that innovation thrives.

About Nigel

Nigel Swycher is co-founder and CEO of LexisNexis Cipher – His background is in law, where he led the IP practice at leading law firm Slaughter and May.

Nigel has been recognized for many years by the IAM 300 as a leader in the field of IP strategy.

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