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Parthiban Srinivasan (VINGYANI, India)
When new technologies become easier to use, they transform industries. That's what's happening with artificial intelligence (AI) and big data. Machine learning is often described as a type of AI where computers learn to do something without being programmed to do it. Deep learning, a subset of machine learning, is proving to work especially well on classification. Big breakthroughs happen when what is suddenly possible meets what is desperately needed. For years, patent analysts have been searching and reviewing terabytes of information, not only patents but also non-patent information. Not only to find prior art but also to identify patents of interest, rate their quality, assess the potential value of patent clusters, and identify potential business partners or infringers. With the rapid increase in the number of patent documents worldwide, demand for their automatic clustering/categorization has grown significantly. Many information science researchers have started to experiment with machine learning tools, but the adoption in the patent information space has been sporadic. In this talk, we aim to review the prevailing machine learning techniques and present several sample implementations by various research groups. We will also discuss how data science compares with machine learning, deep learning, AI, statistics and applied mathematics.