Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Personalized Patent Claim Generation and Measurement   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.

12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat