  This work-in-progress paper proposes a framework to generate and measure
personalized patent claims. The objective is to help inventors conceive better
inventions by learning from relevant inventors. Patent claim generation is a
way of "augmented inventing." for inventors. Such patent claim generation
leverages the recent transfer learning in the Deep Learning field, particularly
the state-of-the-art Transformer-based models. In terms of system
implementa-tion, it is planned to build an "auto-complete" function for patent
claim drafting. The auto-complete function is analyzed from four different
perspectives: extent of generation, generative direction, proximity of
generation, and constraint in generation. Technically, the framework is
composed of two Transformer models. One is for text generation and the other is
for quality measurement. Specifically, the patent claim generation is based on
GPT-2 model and the measurement of personalization is based on BERT model. The
training data is inventor-centric and comes from the Inventors Endpoint API
provided by the USPTO.
