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.
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