AI agents are everywhere right now - writing code, answering questions, summarizing research, even automating full workflows. It’s no surprise that - with the release of our new Deep Research Agent - we’ve been hearing from a lot of people who are exploring how to make agents work for them.

In today’s innovation-driven world, global R&D spending has skyrocketed—tripling since 2000. Patent filings have followed suit, yet the share of R&D budgets devoted to patenting activities (like drafting, filing, and prosecution) has actually been cut in half. What’s behind this paradox, and how can IP professionals adapt?

Recent advancements in AI technology have paved the way for interactive engagement models that are transforming the way IP professionals and innovators work together. These platforms foster real-time collaboration, integrate directly into existing workflows, and allow for tailored experiences based on specific organizational needs. Instead of passively searching databases, users can now engage with tools that adapt to their unique processes, making IP management a more strategic and value-driven endeavor.

FluidityIQ customers can now get detailed novelty assessments, including a complete analysis of prior art, a full feature matrix and links to source documents to maintain connection with source data. Importantly, this novelty assessment is completely embedded within FluidityIQ’s Digital Research Assistant so users can conversationally interact with the report to customize or expand it using natural language. All reports are formatted and can be exported for use in other deliverables.

Within corporate intellectual property (IP) departments, the decision to forego a pre-filing patent search is often justified as a prudent cost-saving measure, given that the United States Patent and Trademark Office (USPTO) conducts its own prior art search during examination. However, the USPTO’s search was never intended to replace an applicant’s due diligence.

When we launched FluidityIQ, we knew that if we were going to build a patent intelligence platform it had to be more than “good enough”. It had to provide real advantages to our customers. Our mantra was and is: “Better. Faster. Cheaper.”

At FluidityIQ, we’ve spent a lot of time and treasure making sure that our patent searching capabilities exceed what’s available in the market. You can learn more about that here. Importantly, we’ve also focused a great deal of resource on what we call the “curation” phase of patent research.  

Outsourced searches are usually executed by creating several compound search strategies across one or more platforms, using a variety of key words, synonyms and Boolean operators to limit the number of patents that need to be reviewed.  This yields a series of disparate results that then need to be combined and analyzed. The resulting output – a patent search report - is a static artifact, representing only a snapshot in time.

Even though FluidityIQ is an AI-native platform, we believe that strong results matter more than any technological feature, function, or architecture. Instead of adding to the hype surrounding all things AI in the current IP landscape, we chose to commission an objective analysis comparing the performance of our platform to two of the leading platforms in the market.

The race is on.  With over $500B being spent on foundational models and supporting infrastructure, the tech giants are jockeying for their place as the go-to provider of Generative AI capabilities.  Surely, it’s an exciting time for us in the tech industry as models continue to leapfrog each other with new capabilities and ever-expanding context windows. 

There is an artificial intelligence (AI) arms race in many industries right now and patent intelligence is no exception. As various providers work to ensure that they are not being left behind, the user community is left sorting through all the noise to determine what kind of solution makes sense for their specific business or legal practice. This can be a difficult task, even with a reasonable understanding of AI capabilities.