What Patent Attorney Fee Awards Really Look Like

This Essay provides an empirical account of attorney fee awards over the last decade of patent litigation. Given the current attention in legislative proposals and on the Supreme Court’s docket to more liberal fee shifting as a check on abusive patent litigation, a fuller descriptive understanding of the current regime is of utmost importance to forming sound patent-litigation policy. Following a brief overview of judicial experience in patent cases and trends in patent-case filing, this study presents analysis of over 200 attorney fee award orders from 2003–2013

Promoting Antibiotic Stewardship

Antibiotics are not always prescribed optimally in the outpatient setting for common respiratory illnesses. Providers also spend time reiterating the same facts to patients about antibiotics and why their use is not warranted for their common cold symptoms. By providing a visual aid that can capture the attention of patients and present concise, easy to retain facts, we may lessen the amount of time providers spend counseling. At the same time, we are able to get patients to contribute to the fight against antibiotic overuse, antimicrobial resistance, and healthcare associated infections.https://scholarworks.uvm.edu/fmclerk/1280/thumbnail.jp

A Neural Network Classifier for the COI Barcode Gene

Mitochondrial Cytochrome C Oxidase subunit I (CO I – to be read as “see – oh one”) is a 658 base pair region in the gene encoding that is proposed as standard barcode for animals. Meaning, the CO I is a special region found in animal DNA that is studied to identify the species of the animal. Currently, there is an implementation of an algorithm called ARBitrator which identifies and extracts these CO I sequences from enormous genes database called GenBank. The ARBitrator is good at extracting the CO I sequences that have better specificity and accuracy as compared to other existing algorithms for CO I sequence identification[1][2]. Now, this project aims at training a neural network to learn the features of the CO I sequences extracted by ARBitrator, so that this neural network can be used in future to further recognize CO I sequences. Effectively, we are aiming to successfully design, train, and use a deep learning neural network to learn to recognize CO I sequences in a supervised way. This is the first time that a neural network is explored and used for this purpose