Natural Language Processing for automatically checking novelty of ideas

XLScout uses Natural Language Processing (NLP), Machine Learning (ML), and Innovation/Scientific principles to deliver actionable intelligence and accelerate innovation by analyzing large patent and research databases. The company is eliminating the pain of manually going through document and quickly providing relevant information to support data-driven strategic decisions. Presently XLScout hosts a data vault of over 130 million patents and 200+ million research publications occupying approximately 8TB of storage. Effectively searching these documents is time-consuming and it commonly requires advanced strategies that a novice searcher may not be familiar with. Moreover, as the database is so large, it is difficult to distill relevant information just by using keyword-based searches. XLScout already has different machine learning techniques for extracting information from these databases, and this project is seeking to make these systems smarter, efficient and scalable by utilizing state-of-the-art deep learning-based models.

Faculty Supervisor:

Eric Yu

Student:

Partner:

XLSCOUT

Discipline:

Computer science

Sector:

Information and cultural industries

University:

University of Toronto

Program: