PatentDom

Abstract

The fast growth of technologies has driven the advancement of our society. It is often necessary to quickly grasp the linkage between different technologies in order to better understand the technical trend. The availability of huge volumes of granted patent documents provides a reasonable basis for analyzing the relationships between technologies. In this paper, we propose a unified framework, named PatentDom, to identify important patents related to key techniques from a large number of patent documents. The framework integrates different types of patent information, including patent content, citations of patents, and temporal relations, and provides a concise yet comprehensive technology summary. The identified key patents enable a variety of patent-related analytical applications, e.g., outlining the technology evolution of a particular domain, tracing a given technique to prior technologies, and mining the technical connection of two given patent documents. Empirical analysis and extensive case studies on a collection of US patent documents demonstrate the efficacy of our proposed framework.

Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis

“…Although semantic MPA proved to improve ranking pertinence by a large margin, there seemed to still large space to improve recall. To achieve this, we guess that it may be helpful to start and guide main path exploration by first ranking and selecting important publications in some way (Bae et al, 2014; Zhang et al, 2014; Tao et al, 2017; Ding et al, 2022).…”

Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow*

“…While utilizing time-frequency domain data proves advantageous for glitch-contaminated data [97][98][99], inherent limitations in the time-frequency resolution and binning may result in the loss of intricate details, thereby influencing the parameter inference. In recent years, some researchers have studied improvements of the network performance from the data fusion of time series and corresponding frequency domain data [100][101][102]. Therefore we employ a dual approach, incorporating both time-domain and time-frequency domain data in the parameter inference process, i.e., temporal and time-spectral fusion normalizing flow (TTSF-NF).…”