215,249 research outputs found
Patent Analytics Based on Feature Vector Space Model: A Case of IoT
The number of approved patents worldwide increases rapidly each year, which
requires new patent analytics to efficiently mine the valuable information
attached to these patents. Vector space model (VSM) represents documents as
high-dimensional vectors, where each dimension corresponds to a unique term.
While originally proposed for information retrieval systems, VSM has also seen
wide applications in patent analytics, and used as a fundamental tool to map
patent documents to structured data. However, VSM method suffers from several
limitations when applied to patent analysis tasks, such as loss of
sentence-level semantics and curse-of-dimensionality problems. In order to
address the above limitations, we propose a patent analytics based on feature
vector space model (FVSM), where the FVSM is constructed by mapping patent
documents to feature vectors extracted by convolutional neural networks (CNN).
The applications of FVSM for three typical patent analysis tasks, i.e., patents
similarity comparison, patent clustering, and patent map generation are
discussed. A case study using patents related to Internet of Things (IoT)
technology is illustrated to demonstrate the performance and effectiveness of
FVSM. The proposed FVSM can be adopted by other patent analysis studies to
replace VSM, based on which various big data learning tasks can be performed
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