During mergers and acquisitions, enterprises need to understand quickly the company’s IP, the IPs of the competitors, and the way the product offerings could affect the technology landscape. For a small team on a budget or deadline, keyword searches and technical expertise in the subject are insufficient to accomplish this. Advancements in Artificial Intelligence (AI) and Machine Learning (ML) are all set to transform how enterprises conduct due diligence at reduced costs.
ML or AI can help IP attorneys identify potentially relevant patents and assess the risk posed by them. Moreover, human expertise in combination with ML or AI is effective for balancing thoroughness, speed, and budget. For example, ML enables conceptual searches on customized search indexes. A type of conceptual search index, Latent Semantic Indexing (LSI), converts the text in patents into mathematical vectors and places them in a multidimensional concept space.
Subsequently, the machine generates a ranking depending on how close a given patent is to a known patent, without entirely relying on keyword or Boolean search. Combining human judgment and a strategy such as LSI with other types of ML or AI drastically reduces the time required for a search, while simultaneously minimizing the risk of missing critical references.
Leveraging ML and AI in combination with keyword, Boolean, and data visualization strategies, enable mergers and acquisitions teams to make big decisions while reducing risk, staying within budget, and finishing on schedule. To this end, a variety of patent research databases and software tools are available for this type of analysis, and they are becoming more accessible for IP attorneys every day.
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