
Arush J. 2025 | BASIS Independent Fremont
- Project Title: Context-AwAre Retrieval of Trademark Publications Using Natural Language Querying Systems
- BASIS Independent Advisor: Ms. Vibha
Generative AI often depends on outdated or incomplete information, which Can lead to false or misleading outputs—What experts call “AI hallucinations.” That might not matter much if you’re just looking for a new dinner recipe, but it’s a serious issue when dealing with patents, where every detail Can influence legal and business decisions. My project tackles this by building a “retrieval-augmented generation” (RAG) system that draws on the United States Patent and Trademark Office (USPTO) database in real time, ensuring the AI Can pull up the latest, most relevant data before generating an answer. This approach is vital because inventors, startups, and legal professionals rely on accurate, up-to-date patent information to avoid costly errors—whether they’re creating a new product, shaping a business strategy, or protecting their intellectual property. By pairing a state-of-the-art language model with real-time retrieval, my system aims to minimize these “hallucinations” while saving countless hours of manual searching, ultimately making patent insights more accessible, trustworthy, and empowering for anyone who depends on informed, rapid decision-making in today’s fast-paced world.