Sitting down to write this article, I’m not sure whether to call it an op-ed, an infomercial, or just an upbeat rant. Whatever the label, one thing is clear: for Fortune 500 companies and start-ups alike, making sense of AI’s role in their legal strategies – patent portfolio management being the focus here – has been a top point of discussion for what feels like years.
In some cases, maybe the discussion tends toward irrational exuberance about using AI to dramatically increase filing volume, improve throughput, reduce costs, etc. In others, exasperation about not quite understanding its capabilities or how it will really help your particular situation. Or, if you’re lucky, a level-headed assessment of where AI can improve efficiency while recognizing its shortcomings, and specifically identifying AI use cases where it is prudent to exercise extra care and caution.
Most likely, it’s simply all of the above.
At any rate, when a client comes to me with a question about AI and patent practice, the response I find myself converging on is straightforward: let’s talk. Not to settle on one of a handful of standard solutions, but because so far, I’m not convinced that those exist. Every patent portfolio involves different technologies, business objectives, competitive goals, and budget considerations. In my experience, the most effective use of AI begins with an open and ongoing dialog to understand those objectives, so that we can identify where AI is most useful to help the client make better patent portfolio decisions, rather than simply making those decisions faster.
This distinction is important – and that’s not just my interest in self-preservation talking. So many of the casual conversations that I’m a part of around AI in patent practice focus on improving drafting efficiency or automating routine tasks. But I think that the greatest long-term value of AI lies elsewhere: building stronger patent portfolios by improving decisions throughout the patent process, from invention harvesting and filing decisions to ongoing portfolio optimization and review. At Conley Rose, we want to use AI less as a replacement for experienced counsel, and more as a force multiplier for it.
What AI can do. And what it can’t.
It’s important to understand that while AI is not a replacement for experienced patent counsel, it is also not an overhyped novelty tool with minimal practical value. I have no problem admitting that in the not-too-distant past, I had hoped for the overhyped novelty tool outcome. But of course, like most technologies, AI’s utility depends on how it is used.
AI can rapidly summarize technical disclosures, compare an invention concept against an existing patent portfolio, identify patterns in prosecution histories, and perhaps uncover trends that might otherwise go unnoticed. Particularly for companies managing large numbers of patent assets, AI can offer significant time savings to collect and analyze this type of information.
However, AI cannot fully appreciate a company’s business priorities, competitive landscape, or risk tolerance. Decisions about whether a particular invention is worth protecting? Not for AI. Whether to file a continuation given a covered product’s roadmap? Nope. These types of decisions require context, judgment, and experience. AI can better inform these decisions, but ultimately these are decisions for which the experienced practitioner’s input will be invaluable.
Supporting Invention Harvesting.
One of the areas where I see the most practical application of AI is in a company’s internal invention harvesting process. Even sophisticated companies often fall victim to letting good inventions slip through the cracks. It’s not necessarily a lack of innovation, but that inventions are not fully identified or documented. Engineers are busy and mostly don’t work with patents on their mind, and so even when invention disclosures are submitted, they may omit key details, alternative embodiments, or a clear explanation of the underlying business problem that the invention solves.
Deployed carefully, AI can help address these issues. For example, AI can assist in preparing targeted questions for inventors to address in their invention disclosure. AI can also help organize engineers’ technical notes into more complete disclosures, and in a style that may be more conducive to the invention disclosure conference with outside counsel. Personally, I have seen AI identify technical inconsistencies and missing details, which when presented to the inventors, spurred meaningful additional conversation around these points and resulted in a better understanding of the invention for me, and a better appreciation of the resulting claims for the inventors. AI may also be useful to compare a proposed invention with an existing portfolio to identify gaps in coverage, continuation opportunities, or complementary applications.
Of course, the recurring theme is that these AI approaches are best used to supplement, not replace, conversations between inventors and outside patent counsel. Some of the most valuable patent applications stem from discussions that reveal commercial objectives, current or future products covered by the invention, or technical details that were not initially present in disclosure materials. Using AI to facilitate these conversations with an experienced attorney can help companies improve the value and quality of their patent portfolio.
Experienced Counsel Still Matters.
With each passing month, AI tools are becoming more capable, which raises the question: is sophisticated legal judgment becoming less important? At Conley Rose, we believe exactly the opposite. AI makes it easy to generate massive amounts of information, but that only increases the value of experienced counsel in sifting through that information to determine what actually matters.
Patent portfolio management is a never-ending series of strategic decisions. What inventions to protect? Where to protect those inventions? How broad should claims for a particular application be? Which patent assets are worth continued investment, and which can be let go of? Do parts of the portfolio create licensing leverage? And on it goes. These are business decisions informed by legal analysis, not mere queries that can be automated away.
Meanwhile, experienced counsel will also be able to recognize the shortcoming of AI output. Hallucinated citations, incomplete analyses, misunderstandings (or misstatements) of the underlying law, and overconfident conclusions are all common characteristics of AI tools. Identifying these shortcomings is more than a proofreading task; it requires the kind of experience that only comes from years of drafting and prosecuting applications, counseling clients, and understanding competitive landscapes.
At Conley Rose, we don’t use AI because it is en vogue. We strive to use AI where it genuinely improves efficiency or allows us to provide analytical insight that goes above and beyond. If there’s one point I hope you take away, it’s that AI isn’t changing the fundamental objective of patent portfolio management. The goal remains the same: build a portfolio that advances your business objectives. AI simply gives us new tools to pursue that goal. Companies that will benefit the most are those that integrate AI with experienced legal judgment to help make strategic decisions related to their patent portfolios.
For companies incorporating AI into patent portfolio management, combining advanced analytical tools with experienced legal judgment can improve invention harvesting, strengthen portfolio decisions, and align patent strategy with broader business objectives. Contact Conley Rose to discuss how your organization can use AI strategically while maintaining the judgment and oversight necessary to build a valuable patent portfolio.