Sumedha Ahuja focuses on strategic patent portfolio management and patent prosecution with specific emphasis on computer software, internet, and artificial intelligence/machine learning inventions. She has successfully drafted and prosecuted over 100 patent applications for both large corporations and startups at the U.S. Patent and Trademark Office and foreign patent offices.

Sumedha’s experience spans a great depth and breadth of technical areas, including telecommunications, business models, mobile applications, advertising and behavioral analysis, search and search result processing, blockchain, IoT, cloud computing, and virtual computing. Her representations frequently involve operating systems and networking, object-oriented programming, natural language processing, and computer vision. In the area of electronic banking and election systems, she assists clients with matters involving encryption and other security measures.

In addition to procuring patents in these fields, Sumedha evaluates third-party patent portfolios, counsels clients on avoiding patent infringement, and mines client patent portfolios to identify assets for licensing, patent infringement counterclaims and divestment. Sumedha has also represented both patent owners and petitioners before the U.S. Patent and Trademark Office (USPTO) Patent Trial and Appeal Board (PTAB) in more than two dozen post-grant proceedings, including inter partes reviews and covered business method reviews. Her experience includes conducting and defending expert deposition as well as drafting successful petitions, patent owner responses, and expert reports. She has also published a leading handbook on post-grant proceedings.

Prior to practicing law, Sumedha was a software engineer at Sapient Corp., where she designed, developed and deployed software solutions for large enterprise clients. As a graduate student at McGill University, Sumedha was a member of both the Mobile Robotics Lab and the Reasoning and Learning Lab, where she focused her research on developing recommendation systems using various machine learning techniques.

Artificial Intelligence (AI) and automated systems can increase efficiency and help reduce human error. However, the National Institute of Standards and Technology (NIST), the White House, and the Equal Employment Opportunity Commission (EEOC) are warning companies that uncritical reliance on AI can have legal consequences, including potentially building in bias that can lead to claims