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Patent citations are one of the most commonly-used metrics in the innovation literature. Leading uses of patent-to-patent citations are associated with the quantification of inventions' quality and the measurement of knowledge flows. Due to their widespread availability, scholars have exploited citations listed on the front-page of patent documents. Citations appearing in the full-text of patent documents have been neglected. We apply modern machine learning methods to extract these citations from the text of USPTO patent documents. Overall, we are able to recover an additional 15 percent of patent citations that could not be found using only front-page data. We show that "in-text" citations bring a different type of information compared to front-page citations. They exhibit higher text-similarity to the citing patents and alter the ranking of patent importance. The dataset is available at patcit.io (CC-BY-4).