Patent analysis has recently been recognized as a powerful technique for
large companies worldwide to lend them insight into the age of competition
among various industries. This technique is considered a shortcut for
developing countries since it can significantly accelerate their technology
development. Therefore, as an inevitable process, patent analysis can be
utilized to monitor rival companies and diverse industries. This research
employed a graph representation learning approach to create, analyze, and find
similarities in the patent data registered in the Iranian Official Gazette. The
patent records were scrapped and wrangled through the Iranian Official Gazette
portal. Afterward, the key entities were extracted from the scrapped patents
dataset to create the Iranian patents graph from scratch based on novel natural
language processing and entity resolution techniques. Finally, thanks to the
utilization of novel graph algorithms and text mining methods, we identified
new areas of industry and research from Iranian patent data, which can be used
extensively to prevent duplicate patents, familiarity with similar and
connected inventions, Awareness of legal entities supporting patents and
knowledge of researchers and linked stakeholders in a particular research
field.

利用图表学习方法分析伊朗官方公报中的专利数据，发现新的行业和研究领域，避免重复专利，了解类似和连接的发明，认识支持专利的法律实体，以及了解特定研究领域中的研究人员和相关利益相关者。

图表示学习用于专利网络分析

Graph Representation Learning Towards Patents Network Analysis

While there has been a recent burgeoning of applications at the intersection
of natural and programming languages, such as code generation and code
summarization, these applications are usually English-centric. This creates a
barrier for program developers who are not proficient in English. To mitigate
this gap in technology development across languages, we propose a multilingual
dataset, MCoNaLa, to benchmark code generation from natural language commands
extending beyond English. Modeled off of the methodology from the English
Code/Natural Language Challenge (CoNaLa) dataset, we annotated a total of 896
NL-code pairs in three languages: Spanish, Japanese, and Russian. We present a
quantitative evaluation of performance on the MCoNaLa dataset by testing with
state-of-the-art code generation systems. While the difficulties vary across
these three languages, all systems lag significantly behind their English
counterparts, revealing the challenges in adapting code generation to new
languages.

本论文提出了多语言数据集 MCoNaLa，评估了基于自然语言指令的代码生成在英语以外的三种语言（西班牙语、日语和俄语）上的表现和挑战。