  In the realm of patent document analysis, assessing semantic similarity
between phrases presents a significant challenge, notably amplifying the
inherent complexities of Cooperative Patent Classification (CPC) research.
Firstly, this study addresses these challenges, recognizing early CPC work
while acknowledging past struggles with language barriers and document
intricacy. Secondly, it underscores the persisting difficulties of CPC
research.
  To overcome these challenges and bolster the CPC system, This paper presents
two key innovations. Firstly, it introduces an ensemble approach that
incorporates four BERT-related models, enhancing semantic similarity accuracy
through weighted averaging. Secondly, a novel text preprocessing method
tailored for patent documents is introduced, featuring a distinctive input
structure with token scoring that aids in capturing semantic relationships
during CPC context training, utilizing BCELoss. Our experimental findings
conclusively establish the effectiveness of both our Ensemble Model and novel
text processing strategies when deployed on the U.S. Patent Phrase to Phrase
Matching dataset.
