Thomson Reuters and Sterne Kessler Show What Fiduciary-Grade AI™ Can Do for Patent Law

Thomson Reuters featured its partnership with Sterne Kessler in “Thomson Reuters and Sterne Kessler Show What Fiduciary-Grade AI™ Can Do for Patent Law,” discussing the Patent Claim Eligibility Analyzer developed to help attorneys evaluate Section 101 patent eligibility through an AI-driven workflow inside CoCounsel Legal.

In legal work, precision is non-negotiable. Attorneys remain accountable for every output, and even small errors can affect case outcomes. A missed precedent, unsupported argument, or flawed analysis can change the direction of a matter.

That accountability demands a higher standard for artificial intelligence. Thomson Reuters defines this as Fiduciary-Grade AI™, built for professionals who operate under duties of care, regulatory oversight, and real-world liability. It’s the foundation behind our partnership with Sterne, Kessler, Goldstein & Fox.

“Our forward-deployed engineers worked side by side with Sterne Kessler’s IP litigators, co-building in rapid iterations. That is a fundamentally different model from how legal technology is typically developed. It is the first proof point of what becomes possible when a firm’s internal intelligence becomes a repeatable, scalable product.” Steve Assie, General Manager of Global & Large Law Firms, Thomson Reuters

The result is the Patent Claim Eligibility Analyzer, an AI-driven workflow inside CoCounsel Legal that helps attorneys evaluate Section 101 patent eligibility using trusted legal content and practitioner-driven analysis. It demonstrates how Fiduciary-Grade AI supports legal work that must be accurate, transparent, and defensible in practice.

Why Section 101 Demands a Higher Standard

Section 101 patent eligibility often determines litigation outcomes, sitting at the center of most utility patent disputes and shaping the trajectory of a case early on. In practice, the analysis is difficult to manage because key concepts lack precise definitions. Outcomes depend heavily on how precedent is identified and applied. Missing a critical case or misinterpreting prior rulings can weaken an argument from the start. At the same time, clients expect fast answers within fixed-fee structures, making it difficult to navigate or explain that uncertainty.

For both patent owners and defendants, the workflow is similar and resource-intensive. Teams often assign a junior associate, spend hours or days identifying relevant precedents, and still question whether they captured everything. This process introduces variability and makes consistent, high-quality outcomes harder to achieve. These constraints show where traditional methods fall short and where a more structured, defensible approach is needed.

Practitioner-built AI Outperforms Traditional Models

Traditional legal technology often follows a familiar pattern. Technologists identify a problem, build a solution, and bring it to market, with practitioners contributing after development. That model can fall short in workflows like patent eligibility analysis, where legal reasoning and precedent interpretation are central to the outcome.

“Patent eligibility analysis depends on our access to accurate, complete information so we can make sound strategic decisions. AI is helping us do that more efficiently, surfacing relevant data faster and enabling more informed, timely guidance for our clients.” Dan Block, Director, Sterne, Kessler, Goldstein & Fox

The Patent Claim Eligibility Analyzer applies a practitioner-built approach designed to address these gaps. It guides attorneys through Section 101 analysis by connecting claims to relevant case law, assessing precedent, and explaining how prior decisions support or challenge eligibility positions. Instead of relying on manual, time-intensive research, it creates a more consistent and transparent way to evaluate complex questions while aligning analysis to how the courts assess patent eligibility.

Thomson Reuters contributes AI capabilities, legal expertise, and authoritative content to structure and refine each step. Fiduciary-Grade AI improves legal outcomes by centering credentialed subject matter experts in workflow design and delivering the legal grounding required for defensible analysis.

See how Thomson Reuters and Sterne Kessler are making patent eligibility analysis faster and more reliable in our recent case study.