Posts in Business

Have you ever drafted a claim set with a second claim that began, “the system of claim 2, wherein…” when you meant to write “the system of claim 1”? It’s embarrassing because every first-year patent attorney knows that a dependent patent claim cannot depend on itself. However, making the error is inevitable when you draft a large number of patent applications. The good news is, if you upload such a claim to today’s Patent Center (where patent applications are filed), you will be provided with the following alert: “The claims appear to contain an improper dependency with at least one claim that depends on a missing or canceled claim. Please review and revise if necessary”. How beautiful is this? Now you can self-correct before your patent application is even filed. Ten years ago, you would have to go back and forth with a patent examiner to correct the error.

Managing patent portfolios requires investment. There are significant costs associated with both building and maintaining patent portfolios, but all too often only a fraction of their potential business impact is ever realized. While obtaining and maintaining weak patents is a real concern, the strength of any particular patent, family or portfolio is not always tied to overall strength. Frequently, the problem is that the organization does not really know what it owns, why it owns what it does own, where patents fit from a strategic perspective, and whether the assets can be credibly used to support any commercial outcome.

In the latest episode of IP Innovators, host Steve Brachmann sits down with David Hyams, Co-Founder and Chief Business Development Officer of Longship Legal, to explore what it looks like to build an IP practice around business value rather than patent volume. Drawing on a career that spans big law in Boston, in-house roles at Bose Corporation and AOL, and a cleantech startup, Hyams makes a case that the most important questions in IP strategy have nothing to do with patentability, and everything to do with understanding what a company is actually trying to win.

Most patent portfolios are overbuilt and under-managed. That is not a criticism of any particular company or patent department. It is simply the predictable result of how patent portfolios are created. Companies innovate. Business leaders demand more filings. Engineers generate invention disclosures. Outside counsel prosecute applications. Patents issue. Then years pass, products change, markets move on, competitors pivot, and strategic priorities evolve. Often—if not frequently—the patent portfolio remains the same, as if legacy assumptions and strategy remain relevant even though they no longer match business or market realities.

To compete in artificial intelligence (AI) markets, emerging companies must choose one of two routes: the capital-intensive route entails buying compute and datasets to build in-house foundation models and refining them into agents for specific use cases. Alternatively, emergents can license pre-trained models and lease compute to focus on developing applications for the end user, whether that is a solo software developer or an entire business domain.

“Should we insource IP work?” This perennial question is posed by in-house professionals and organizational leaders in corporations, universities, and other institutions—and dreaded by outside IP counsel, who fear loss of insourced client business. Deceptively binary and straightforward, the insourcing question often can’t be answered without in-house teams first exploring a host of underlying considerations. Their decision-making calculus may confront grey areas and vexing tradeoffs, ultimately coming down to rough cost-benefit analyses and gut instincts.

Artificial intelligence has moved beyond the experimental phase in legal practice. The legal industry is no longer debating whether lawyers can or should use AI tools, or whether AI will affect the economics of law firm and in-house legal department operations. Those questions have been answered. AI is already reshaping how legal work is performed, how legal departments manage demand, how law firms are expected to price services, how patent teams analyze portfolios, and how clients evaluate outside counsel.

Artificial intelligence (AI) is moving faster than traditional intellectual property (IP) strategy was designed to handle. The issue is not simply speed, although speed is certainly part of the problem. The deeper challenge is that AI innovation does not fit neatly into the legacy IP operating model. The assets, development cycles, regulatory environment, and commercial pathways are all different. And the value drivers are increasingly distributed across a spectrum of AI-related intangible domains, which include patents, trade secrets, data rights, software architecture, licensing models, and customer contracts.

A recent U.S. Court of Appeals for the Federal Circuit decision applying California trade secret law offers a timely reminder that published patent materials cannot easily be recast as trade secrets. In International Medical Devices, Inc. v. Cornell, the Federal Circuit reversed trade-secret liability and vacated related damages and injunctive relief after concluding that the plaintiffs had not shown protectable trade secrets under the California Uniform Trade Secrets Act.

This week on IPWatchdog Unleashed, I have a candid conversation with Melissa Silverstein about both IP strategy and the human side of IP, including a discussion of the struggles that some attorneys have with substance abuse. The first half of the conversation centers on a clear market correction in intellectual property strategy: portfolios are being forced to operate like business assets rather than legal inventory…. The conversation then pivots sharply to the human dimension of the profession, where Silverstein’s current work is focused. Drawing on her own experience, she addresses the prevalence of substance abuse, burnout, and mental health challenges among high-performing attorneys.

Today, the European Union Intellectual Property Office (EUIPO) published a study exploring challenges faced by EU small- and medium-sized enterprises (SMEs) in obtaining financing by offering intellectual property (IP) as collateral. Set against the backdrop of the EU’s recently launched Savings and Investment Union (SIU) program, the EUIPO’s study identifies several structural barriers preventing SMEs from obtaining IP-backed financing and concludes with a series of policy recommendations designed to address the SME credit gap and unlock tremendous economic value for the wider EU market.

IPWatchdog is happy to announce several leadership promotions to support its continued growth and strategic expansion. Renée C. Quinn has been named President, Katarzyna Kryca has been promoted to Senior Vice President, and Morgan Connell has been promoted to Director of Programs and Strategic Partnerships. Founder Gene Quinn will continue to serve as Chief Executive Officer.

For decades, management scholars and practitioners have grappled with what I call the “knowledge problem” in organizations—the stubborn difficulty of codifying and transferring expertise that resides in individual employees’ heads and habits. The most valuable organizational knowledge has always been tacit: the judgment calls, the contextual adaptations, the intuitive “feel” for how things get done. This knowledge walked out the door every evening and, more problematically, departed permanently when employees moved to competitors.

I have spent most of my professional career talking to patent practitioners about AI. For years, the conversation was about whether AI could be trusted, whether it was ready, and whether it would actually change how patent work gets done. I have watched the profession move from skepticism to curiosity to cautious adoption to – in 2026, for the first time – something that feels like acceptance. Questions that once provoked heated debate at conferences now feel almost trite. Nobody is really questioning whether AI has a place in patent practice anymore. The question that has replaced it is harder and more consequential: