This article draws on a recently conducted webinar. Three practitioners discussed how patent analytics and AI are changing the way intellectual property (IP) professionals serve their clients. Dr. Nina Müller is a certified patent engineer and Project Manager at the Tech Center at Gottschald Patent Attorneys in Düsseldorf. She combines patent analytics, AI, and technical expertise to support clients with strategic IP decisions. Nicholas Valentino is U.S. Head of Customer Success at LexisNexis Intellectual Property Solutions. He works with law firms and corporate IP teams across the United States. The session was moderated by Isumo Bergmann, Senior Customer Success Manager at LexisNexis Intellectual Property Solutions, who works with law firms and corporate IP teams across Europe and Asia. The conversation covered three areas: how client expectations around IP advice are shifting, what strategic patent analysis looks like in practice, and how AI tools are changing the speed and scope of what IP teams can deliver. The examples and observations below come directly from that discussion.
When clients come to Gottschald with an IP challenge, they rarely frame it in terms of patents. They come with business concerns. Are we exposed? Where should we focus? Who should we be watching? The patent data is the means; the strategic answer is the end.Valentino sees the same pattern across law firms and corporate IP teams in the United States. “Historically, the deliverables used to just be the charts, the tables, the graphs. Now those are just supplemental to actually answering the outcomes of those business questions.” The audience for those answers has also grown. Dr. Müller noted that it’s no longer just the IP department receiving the work, and increasingly it includes C-level executives who want clear strategic direction, presented in a way that connects patent intelligence to competitive reality.
During the webinar, Dr. Müller shared two concrete examples of the kind of analysis that now forms a larger part of strategic patent practice. Example 1: Finding the competitors who might be infringing your patentsOne question Gottschald hears frequently from clients: Are there competitors infringing our patents? It’s difficult to answer through intuition or simple filing searches, but with citation analysis in PatentSight+, it becomes tractable. For one case, Dr. Müller looked at the landscape of speech-controlled surgical robotics, examining which companies had built strong portfolios in the space and, critically, whose earlier technology they were building on. Johnson & Johnson emerged as a dominant player, having steadily grown one of the largest portfolios in the area since around 2017. The more revealing finding came from the citation data: approximately 15 to 16 of Intuitive Surgical’s patent families held Category X prior art status against Johnson & Johnson filings, meaning Intuitive’s patents were novelty-destructing for around 20% of J&J’s portfolio in this space.
That signal indicated Johnson & Johnson was heavily reliant on Intuitive Surgical’s foundational technology. Cross-referencing with market reality confirmed it: J&J was actively developing a robotic surgical system to challenge Intuitive’s da Vinci platform.The analysis didn’t prove infringement; that requires the detailed claim-mapping work of technical experts and patent attorneys. But it pointed the client toward the right families to monitor and the right products to evaluate for overlap. “Analytics gets you to the more relevant families,” Dr. Müller explained. “It gives you an idea of which are the potentially relevant products. Then you bring in the expertise to do the detailed work.”
Example 2: Spotting a technology leader and who’s following
The second example addressed a different kind of question: how do you identify who’s setting the direction in a technology area, and who’s moving in behind them? Gottschald identified a strong Garmin patent family that attracted heavy citation activity from a single company. Looking at when that company built its citation-heavy portfolio and breaking it down by CPC classification, two distinct waves emerged: an early phase aligned with sports and fitness applications (the iPhone era) and a later phase focused on sensor technology and health diagnostics (the Apple Watch era). The company was Apple, and the patent data had mapped its product trajectory years before the products reached the market.Watch the webinar to see the full analysis with charts.“You can see development trends long before an actual product enters the market,” Dr. Müller noted. “Patent families come up long before the actual market launch.”For a client asking whether to enter a particular space, this kind of analysis provides useful context, including who the established players are, how the technology has evolved, and where adjacent areas may be less crowded.
When the webinar audience was asked to identify the biggest barrier to AI adoption in patent practice, reliability and accuracy drew 42% of responses, and confidentiality concerns drew 33%. Together, those two accounted for three-quarters of all votes, well ahead of cost, lack of internal expertise, or demonstrating client value. AI tools that produce different results when a search is rerun or when prompting changes slightly create real problems for professionals whose advice must be defensible. And in an environment of sensitive client data and attorney-client privilege, where information goes is not a minor consideration. LexisNexis® Protégé™ in PatentSight+™ was designed with both of those concerns in mind. Rather than layering generative AI on top of unverified open-source patent data, Protégé grounds every output in the curated, harmonized patent data that has powered PatentSight+ for years, including verified ultimate ownership data, legal status information, standardized family building, and the scientifically developed Patent Asset Index, a portfolio valuation metric. Dr. Müller pointed directly to owner harmonization as a meaningful differentiator: “It’s manually checked who owns a certain patent family. This takes a lot of noise out of the data.”
Nicholas Valentino
Head of Customer Success, Americas, LexisNexis Intellectual Property Solutions
On confidentiality, Valentino emphasized, “Any information put into the system is not being used to train those commercial products. We have agreements in place with those organizations that any data put in by our users cannot be used for training the next model or for review. It’s the highest level of confidentiality.”
Dr. Müller demonstrated Protégé in PatentSight+ live during the webinar, analyzing how patent portfolios related to large language models had developed since 2016. She typed the question in plain language. Protégé interpreted it, constructed a structured PatentSight+ search string, and presented its reasoning. This included the method applied, the assumptions made, and the limitations of the approach. The resulting search was fully visible, the logic transparent, and the analysis could be transferred directly into PatentSight+ for deeper study. The output showed portfolio growth aligned with the launch of ChatGPT. Breaking the results down by CPC class surfaced a less obvious finding: energy efficiency and climate change mitigation as a significant classification, reflecting the infrastructure demands of training and running large language models. Every step was traceable, which Dr. Müller noted mattered when explaining results to a client months or years later.
Dr. Nina Müller
Certified Patent Engineer and Project Manager, Tech Center at Gottschald Patent Attorneys
“With Protégé in PatentSight+, I will always be able to explain, even five years later, to a client why a certain patent family came up in the result set, or why it did not,” Dr. Müller said
In a professional context where outputs need to be defensible, traceability matters. Valentino described the practical difference from a general-purpose LLM this way: “Protégé in PatentSight+ is specifically built for strategic IP decision-making. Unlike a generic commercial model that knows a little about a lot, Protégé is specifically informed on IP decision-making use cases and data. We’ve done all the upfront work, so when you enter your question, the system identifies the context from an IP professional’s perspective and provides an analysis based on that. You get true efficiency gains because you’re not doing all that upfront work with the generic LLM.”
For practices managing growing demand from IP departments and executive stakeholders alike, the efficiency gains are meaningful.Gottschald is already building toward a further step: an agentic AI suite that can access Protégé alongside other tools in their legal tech platform, passing results between systems to automate workflows that today require significant manual effort. Their vision is landscaping with Protégé in PatentSight+, feeding directly into competitor monitoring, which feeds into the IP search environment, all connected and traceable.How is your company or practice adapting to the disruption brought about by AI in patent analytics? If you prioritize reliable data and defensible analyses, request a demo to get a first-hand experience of Protégé in PatentSight+.
