Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult to measure through traditional means, machine learning offers a promising set of techniques for evaluating novelty, summarizing contributions, and embedding semantics. In this paper, we introduce the Harvard USPTO Patent Dataset (HUPD), a large-scale, well-structured, and multi-purpose corpus of English-language patent applications filed to the United States Patent and Trademark Office (USPTO) between 2004 and 2018. With more than 4.5 million patent documents, HUPD is two to three times larger than comparable corpora. Unlike other NLP patent datasets, HUPD contains the inventor-submitted versions of patent applications, not the final versions of granted patents, allowing us to study patentability at the time of filing using NLP methods for the first time. It is also novel in its inclusion of rich structured data alongside the text of patent filings: By providing each application’s metadata along with all of its text fields, HUPD enables researchers to perform new sets of NLP tasks that leverage variation in structured covariates. As a case study on the types of research HUPD makes possible, we introduce a new task to the NLP community -- patent acceptance prediction. We additionally show the structured metadata provided in HUPD allows us to conduct explicit studies of concept shifts for this task. We find that performance on patent acceptance prediction decays when models trained in one context are evaluated on different innovation categories and over time. Finally, we demonstrate how HUPD can be used for three additional tasks: Multi-class classification of patent subject areas, language modeling, and abstractive summarization. Put together, our publicly-available dataset aims to advance research extending language and classification models to diverse and dynamic real-world data distributions.

Unmanned aerial vehicles (UAVs) have the potential to significantly impact early detection and monitoring of plant diseases. In this paper, we present preliminary work in developing a UAV-mounted sensor suite for detection of citrus greening disease, a major threat to Florida citrus production. We propose a depth-invariant sensing methodology for measuring reflectance of polarized amber light, a metric which has been found to measure starch accumulation in greening-infected leaves. We describe the implications of adding depth information to this method, including the use of machine learning models to discriminate between healthy and infected leaves with validation accuracies up to 93%. Additionally, we discuss stipulations and challenges of use of the system with UAV platforms. This sensing system has the potential to allow for rapid scanning of groves to determine the spread of the disease, especially in areas where infection is still in early stages, including citrus farms in California. Although presented in the context of citrus greening disease, the methods can be applied to a variety of plant pathology studies, enabling timely monitoring of plant health-impacting scientists, growers, and policymakers.