Universal Sensors (United States)

A trademark may be a word, phrase, symbol, sound, color, scent or design, or combination of these, that identifies and distinguishes the goods or services of a particular source from those of others. Obtaining a trademark is a complex, time intensive and costly process that involves varied steps before the trademark can be registered including searching prior trademarks, filing of the trademark application, review of the trademark application and final publication for opposition by the public. One of the crucial aspect in the review of the trademark application is determining the international classes of the filed marks based on their goods and services description. Currently, the process of identifying the international classes of a filed mark is performed manually in the United States Patent and Trademark Office (USPTO) and takes significant amount of time. Recently, word embeddings and deep neural networks (DNNs) have revolutionized the field of computer vision and demonstrated excellent performance in various natural language processing (NLP) tasks such as machine translation, speech recognition, sentence and document classification etc. to name a few. In this study, we explored fastText and different neural networks such as Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), bidirectional versions of both LSTM and Gated Recurrent Unit (GRU) and Recurrent Convolutional Neural Network (RCNN) to automate the international class identification for the filed marks based on their goods and services description. Overall, it was found that the trademark word embeddings with RCNN model outperformed the other models. Our study thereby seeks to provide a solution towards the manual, time intensive and laborious process of identifying international classes for the trademarks.

Amperometric glucose sensors have been assembled by immobilizing glucose oxidase on Nucleopore polycarbonate membranes via glutaraldehyde crosslinking. To eliminate electrochemical interferences, novel blocking membranes were used that are permeable to hydrogen peroxide, making them interference free, highly stable, reproducible, and with extended linearity for glucose up to 50 mM.