Rethinking Prior Art Searches

Inefficient prior art search is one of the most frustrating challenges in patent due diligence today. Legal teams spend tens of thousands of dollars and face long lead times for outsourced searches that often yield incomplete results. And once delivered, these searches frequently need further refinement by patent attorneys or through iterative feedback with search agents. The process is cumbersome, costly, and wastes valuable time and resources.

Advancements in machine learning (ML) and artificial intelligence (AI) are now poised to solve these inefficiencies. Although automated prior art searching has been studied for over two decades, key limitations have hindered its adoption—until now. Below, we explore three major hurdles and how new AI technologies, specifically large language models (LLMs), are overcoming them.

1. Semantic Understanding and Claim Invalidity

In patent litigation, the goal is not just to find relevant prior art—it's about uncovering prior art that can invalidate a patent’s claims. This requires a nuanced semantic understanding of patent texts, something that early models struggled with.

Traditional models lacked the power to break down the complex nuances of patent claims, leaving attorneys to handle these tasks manually. LLMs, however, have fundamentally changed this. With their ability to understand and analyze intricate patent language, these models can classify prior art by its potential to invalidate claims. While human expertise remains essential, LLMs take much of the burden off manual processing, enabling a more efficient semi-automated workflow.

2. Evaluating the Quality of Prior Art Searches

Patent data is vast and accessible, but the real challenge lies in evaluating the quality of identified prior art—whether it can truly invalidate a claim. Without proper evaluation, search results often lack reliability.

To tackle this, our team has built extensive evaluation datasets from office action citations and inter partes review (IPR) decisions. These datasets allow us to fine-tune our AI models, focusing on delivering not only relevant but high-quality prior art, assessed against real-world legal outcomes. This rigorous evaluation process ensures that our search results meet the highest standards, minimizing the need for manual quality checks.

3. Scaling Patent and NPL Searches

The global patent corpus is massive and constantly growing. Add the complexity of Non-Patent Literature (NPL), and it's clear that storing, indexing, and searching this data requires scalable infrastructure.

Over the past year, we’ve invested in building a system capable of scaling to over a billion publications. By combining powerful databases with high-performance search algorithms, we’ve ensured that our system can quickly process vast datasets, providing patent professionals with faster and more efficient search capabilities.

Ready to Transform Your Prior Art Searches?

If you're frustrated by the high costs and inefficiencies of traditional prior art searches, AI-driven tools offer a new path forward. By leveraging LLMs, scalable infrastructure, and rigorous quality evaluation, we’re making prior art searches faster, cheaper, and more reliable than ever.