IP search in the AI era: Reality vs. Hype

Woman with brown hair wearing glasses on head, a black top, and silver earrings against a plain background.
Kathy Van der Herten
Director of Product Management, CAS
Portrait of a man with short gray hair, beard, and glasses, wearing a dark t-shirt, in front of a beige background.
Don Swartwout
Senior AI Scientist

Episode notes

Is AI revolutionizing the IP industry? In this podcast episode, CAS intellectual property and data science experts, Kathy Van der Herten and Don Swartwout, Ph.D., discuss benefits, opportunities, and challenges of emerging technologies in IP searching. Listen and learn to distinguish between hype and reality as the panelists explore:

  • How AI and other emerging technologies have played a role in the evolution of the IP space/
  • What unique challenges impact AI applications in IP search.  
  • How IP searchers can leverage AI’s efficiency and power while avoiding critical barriers.
  • What’s next for AI in the IP search world.

Experts

Woman with brown hair wearing glasses on head, a black top, and silver earrings against a plain background.
Kathy Van der Herten
Director of Product Management, CAS

Kathy joined CAS in January 2021 as Director of Product Management for the IP Product solutions portfolio. Kathy has worked in IP for more than 20 years and is a product professional specialized in creating information software solutions for business professionals. Her experience covers both trademarks and patents, and she is based in Belgium.

Portrait of a man with short gray hair, beard, and glasses, wearing a dark t-shirt, in front of a beige background.
Don Swartwout
Senior AI Scientist

Don Swartwout is a machine learning and systems architecture expert with deep roots in scientific data and telecommunications infrastructure. His career spans foundational work at Bell Laboratories and Lucent Technologies — where he contributed to database systems, network surveillance, and programming environments — followed by more than two decades at CAS, where he has applied that systems thinking to data architecture, data science, and machine learning at scale. He holds a Ph.D. in mathematics from the University of Michigan and has taught graduate courses in system architecture and distributed systems at Franklin University.