Customer Success Manager
Cormac is a Customer Success Manager for the U.S. region at LexisNexis Intellectual Property Solutions. Since joining the team after the acquisition of Cipher, he has helped clients apply machine learning, data analytics, and large language models to IP analytics. He holds an MSci in Biochemistry from the University of Cambridge.
Sid Singh is a Customer Success Manager for the US region at LexisNexis Intellectual Property Solutions. He joined through the acquisition of Cipher in 2023, bringing his experience in machine learning and patent analytics. Sid spearheaded risk projects within Cipher's Solutions team, supporting clients through creating & interpreting bespoke risk monitoring frameworks that help clients identify areas of strength and weakness in their portfolios. Sid's academic background focused on working as a researcher in thermoelectric materials. He holds an MSc in Molecular Modelling from University College London (UCL).
IP departments face a difficult balance. They must manage established patent workflows while answering more strategic questions from across the business. Those requests often concern unfamiliar technologies, competitive activity, investment opportunities, or emerging risks. They can sound simple, but producing a defensible answer may require hours of searching, analysis, and interpretation. Our recent webinar on AI-powered patent landscaping examined how artificial intelligence can help close that gap. The discussion focused on translating emerging technologies into strategic patent insights without obscuring the analytical process from the people responsible for the results.
The webinar featured Siddhartha (Sid) Singh and Cormac Cray, Customer Success Managers at LexisNexis Intellectual Property Solutions. Both work at the intersection of machine learning and patent analytics and have prior scientific research experience. Patent attorney and IPWatchdog founder Gene Quinn moderated the discussion. Together, they explored adoption challenges and demonstrated the analytical capabilities of LexisNexis® Protégé™ in PatentSight+™, using examples from the post-quantum cybersecurity technology and humanoid robotics landscapes.
The discussion kicked off by describing the pressures facing IP departments around three needs: working smarter, moving faster, and operating with leaner resources. IP teams still file patents, manage portfolios, monitor competitors, and support prosecution. At the same time, they are also expected to align patenting strategy with R&D goals, assess new markets, identify licensing opportunities, and respond quickly to strategic questions from business leaders.
Siddhartha SinghCustomer Success Manager LexisNexis Intellectual Property Solutions The traditional outlook of an IP department being a cost center has really evolved. Now IP departments need to have a say in determining the future of the business.
Siddhartha SinghCustomer Success Manager LexisNexis Intellectual Property Solutions
The traditional outlook of an IP department being a cost center has really evolved. Now IP departments need to have a say in determining the future of the business.
An audience poll reinforced this shift. The webinar participants answered that smarter analysis is their leading priority, well ahead of speed or headcount concerns. Another poll, taken later in the discussion, identified patent searching and landscaping as the most common areas for AI implementation. These results suggest that IP teams want to go beyond the regular and answer broader questions that could influence business direction.
Siddhartha SinghCustomer Success Manager LexisNexis Intellectual Property Solutions AI can be used to do the majority of the heavy lifting. That frees up the human or the analyst to spend more time on the actual decision-making process.
AI can be used to do the majority of the heavy lifting. That frees up the human or the analyst to spend more time on the actual decision-making process.
The two demonstrations showed how the same system can support different levels of analytical depth. Sid began with a direct business question about post-quantum cybersecurity. He asked which companies had the strongest portfolios in the field, initially using portfolio size as the basis for comparison.
Protégé translated the question into a Boolean search, surfaced the reasoning behind it, and presented leading portfolio owners. The output also included comparisons based on the LexisNexis® Patent Asset Index and its component indicators. This revealed an important analytical point: the largest portfolio was not necessarily the most influential. The demo also showed patent family summaries organized by technical content and claim coverage.
Cormac used a more detailed prompt to examine humanoid robotics, a landscape we recently covered in an article. He defined three technology areas: interaction systems, control and planning, and architecture and morphology. He then requested leading owners by portfolio size and Patent Asset Index, 10-year portfolio trends, invention locations, supporting scientific context, and a consolidated view.
This approach resembled a repeatable analyst workflow rather than a one-off question. The output compared technology segments, showed how portfolios changed over time, and highlighted the difference between portfolio volume and impact. It also demonstrated how an analyst can specify the method, measures, and final format before the system begins its work.
Together, the examples showed that accessibility and analytical control need not be opposites. A non-specialist can begin with a business question, while an experienced analyst can define a more rigorous process. In both cases, the user can inspect the steps and continue the analysis through follow-up prompts.
Watch the full webinar recording to see how Protégé in PatentSight+ answers a simple one-line question and a more complex, detailed query.
The webinar also addressed why many AI experiments fail to become dependable team workflows. Cormac grouped the main barriers into transparency, data, and consistency. Each of these affects whether an analyst can validate an output and defend it to colleagues.
Transparency becomes critical when AI filters documents, constructs a search, or selects evidence. Analysts need to understand what the system did, why it chose that approach, and where limitations may remain. Otherwise, they cannot confidently present the work as their own.
Protégé in PatentSight+ addresses this need by showing a summary of the reasoning and the plan used to answer each question. For example, showing the Boolean search string behind a landscape gives users a familiar way to review scope, test assumptions, and refine the query.
Data quality and context form the second requirement. During the webinar, the speakers explained that patent data results in Protégé come from the curated PatentSight+ database. The analysis also incorporates established, industry-proven indicators such as Patent Asset Index, Market Coverage, and Technology Relevance. Additionally, by pulling in relevant external context from trusted public sources, Protégé in PatentSight+ can deliver a holistic interpretation when answering a business question that requires information beyond patent records.
General-purpose AI tools can often produce different results across users, even when the underlying task is similar. Whereas purpose-built tools like Protégé use defined patent analytics skills and tools to create a more repeatable analytical pathway. That enables clearer comparisons between projects and reduces randomness in responses due to individual prompting styles.
Cormac CreaghCustomer Success Manager LexisNexis Intellectual Property Solutions The aim is for this to be repeatable, easily verifiable, and easily readable by a human, avoiding a black box solution.
Cormac CreaghCustomer Success Manager LexisNexis Intellectual Property Solutions
The aim is for this to be repeatable, easily verifiable, and easily readable by a human, avoiding a black box solution.
The webinar discussion did not frame AI as a replacement for patent expertise. Instead, it offered AI as a way to shift effort toward higher-value work. Analysts still define the question, review search logic, test assumptions, assess relevance, and connect patent evidence to business context. This human role is especially important when the technology is emerging or the decision carries material consequences. A structured output can accelerate the first stages of analysis, but expert judgment determines whether the search fits the question and whether the conclusion supports action.
Gene also emphasized that effective use develops through iteration. Over time, users will learn which context matters, how much detail to provide, and when to challenge the system’s interpretation. Features like the reasoning summary give them a practical checkpoint for that exchange.
For IP teams, the central takeaway is clear. AI-powered patent landscaping can shorten the path from a complex technology question to a structured analysis. Its business value depends on more than speed. Transparent logic, trusted patent data, consistent methods, and informed human review turn an AI response into an insight that stakeholders can use.
Watch the full webinar recording to see both demonstrations and explore how different prompting approaches can support your patent analytics workflows.
Customer Story
“Once people see the benefits, nobody will want to go back.” That’s how the analyst described using Protégé to cut days of portfolio analysis down to minutes. Learn what changed for them.
