The Evolution of Legal Technology: Lessons from Computing History for AI Adoption

When Everything Changed: The Major Exponential Advance

As a kid growing up in the 1980s, I was captivated by the emerging world of home computers. I had a TI-99/4A first and later upgraded to a Commodore 64.  I spent countless hours as a child writing programs in BASIC, trying to make these first home computers do various things, and absorbed in the possibilities they represented.  In reflecting on the evolution of computers over the decades since those childhood experiences, I’ve come to the realization that one of the major exponential advances in computer technology for users was when home computers became widely available.  That was the biggest change in our lives that came from computers and, in my view, far more transformative than any of the incremental improvements that followed.

Yes, we’ve seen remarkable developments since then.  Storage technology evolved gradually over a long timeline—from cassette tapes, to floppy disks, then hard drives, and now solid-state drives.  Graphical user interfaces emerged, transforming how we interact with machines. The internet revolutionized information access and communication.  But for the most part, my perception has been that progress has been mostly linear since early home computers became available.  These subsequent advances primarily allowed more people to more easily take advantage of computers and increased the amount of information and entertainment easily available, rather than fundamentally advancing what computers could do.  The quantum leap had already occurred—universal access to computing power.

The AI Parallel: History Repeating Itself

The evolution of LLMs will likely be similar.  The major exponential advance of widely available access to LLMs has already occurred, made possible by the fact that most people have access to a computer and the internet.  You might have better LLM access if you pay for it, but even for free you can access LLMs that do a reasonable job with basic tasks involving non-confidential information.

The computing revolution of the 1980s taught us something crucial: early adopters who learned to work with accessible but initially limited technology gained lasting advantages.  Those who embraced home computing when it first became available—despite its limitations, despite the primitive interfaces, despite the learning curve—positioned themselves to leverage every subsequent improvement.

It is unlikely that the LLMs we have access to are going to rapidly get dramatically better.  We will more likely see a relatively linear increase in LLM capability as time moves forward.  As such, I believe there’s no better time than now to learn how to use LLMs and to learn how to incorporate what they can do into your workflow.

The Transformation Is Already Happening

The question isn’t whether LLMs will transform patent practice—they already are.  A few practical examples of ways that LLMs are being used in patent prosecution include:

• Document Analysis. Documents such as prior art references can be uploaded to LLMs for targeted questioning.  LLMs can be used analyze inventor meeting transcripts and quickly generate organized disclosures.  LLMs can be used to summarize invention disclosures or lengthy experimental sections to quickly understand the subject matter.
• Strategic Patent Prosecution. When responding to prior art rejections, LLMs can help identify distinguishing features across the specification and dependent claims, and can assist with analyzing whether to argue directly against prior art rejections or pursue claim amendments.
• Addressing Chemical Structure Challenges. Even in focused areas like chemical patent prosecution, AI is proving valuable.  While some current limitations exist with complex chemical structure recognition, workarounds including recognition of text-based representations of chemical structure such as SMILES and recognition of certain chemical image file formats are already available.

AI should be viewed as an augmentation, not a replacement.  It would be useless without you.  It is not a way to replace you—it is a way to make your work product better.

The Optimal Learning Window Is Now

Just as early home computer users gained lasting advantages by learning to work with initially primitive but universally accessible technology, patent practitioners who embrace AI now—while it’s still emerging but broadly available—will be best positioned for the future.

Patent examiners are also beginning to use AI tools, particularly in searching processes.  This creates what could be characterized as an “arms race” scenario where both sides of patent prosecution are leveraging AI capabilities.  Understanding and mastering these tools now provides a strategic advantage that will only become more valuable as adoption accelerates.

If this historical perspective resonates with you and you’re ready to move beyond theory to practical application, I encourage you to watch my full presentation entitled “Practical Use of AI in Drafting and Prosecution of Chemical Patent Applications.” The presentation goes far beyond the conceptual framework discussed here, offering concrete explanations of AI tools in action, specific prompting strategies, and real-world examples that you can adapt to your own practice immediately.  Whether you’re a seasoned practitioner looking to enhance your work product quality or someone just beginning to explore AI’s potential in patent work, the presentation provides the practical roadmap you need to start implementing LLM tools effectively.

Dr. Nicholas P. Lanzatella is a Principal at SLW. If you would like to discuss this article further, please contact him at nlanzatella@slwip.com. His recent webinar, “Practical Use of AI in Drafting and Prosecution of Chemical Patent Applications,” is available on the SLW Institute.