Searching for prior art in patent law cases is a challenging problem requiring shifting through large corpora of unstructured content to find critical words, phrases and documents that are relevant to a case. Keyword searching and reading through sometimes thousands of documents is time consuming and not comprehensive.
With Cortex users can apply advanced deep learning analytics to Big Data scale text corpora that have been easily integrated in the Cortex platform to search with greater efficiency and effectiveness for the critical insights and phrases relevant to their case. Cortex can detect the similarity of sentences even if there is no keyword overlap between them, helping the user to identify similar meaning in text instead of just the same words. Furthermore, Cortex’s Neural Concept Recognition AI technology can train on concepts relevant to a case (e.g. people’s names, company names, technical concept names) and scan the entire rest of the information corpus to find novel instances of each of those types of concepts.