is a business development consultant and currently serves as Vice President, Business Development for ICmasters, a technical services company based in Ottawa, Canada, serving the global semiconductor industry with advanced reverse engineering and Intellectual Property support services. His career and academic interests have always spanned technology and the humanities, and are expressed through an avid enthusiasm for helping technology companies successfully commercialize their innovations in the global marketplace.
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Contemporary AI technology of the kind one has increasingly heard about in recent years is based on machine learning and deep learning methodologies. These use large amounts of computing power to crunch thousands of sample input-output pairs to train adaptable data structure models. Eventually, they are able to produce their own correct outputs when presented with an nth + 1 input. These can be thought of as questions and answers. If an AI model is given, say, 10,000 sample questions with correct answers, it will be able to correctly answer the 10,001st question by itself. Once trained, computing requirements are low. Due to the nature of the methodology, AI is appropriate for situations that involve repetitive decision-making processes. For one thing, many existing examples of correct decisions must be available during the training. Further, after the training phase, a system is applied to similar situations over and over again. Because of this, the application space for AI is sometimes overblown. However, once understood, this limitation usefully directs our attention to instances of decision-making that can be automated or made more efficient using AI. If we consider patent portfolio management in terms of constituent decision-making processes, we might be able to identify which of them are appropriate for the application of AI.
An important part of any patent portfolio manager’s work is to understand which patents are likely to be implemented in real-world technology. We can think of the process of matching patents to products as having three levels of progressively precise analysis. The investment in time and resources required to arrive at useful documentation at each level increases as the level of analysis increases. Accordingly, access to higher levels of product matching analysis will tend to enable higher monetary returns if the associated documentation can be brought to bear effectively in the intellectual property marketplace. To begin with a bit of background, there is a strong correlation between the value of a patent and the likelihood that the technology it describes is instantiated in real-world commercial products. Any methodologies that can provide insight into this likelihood therefore provide information on the value of a patent.