\section{Introduction}
\label{intro}
 

Classifying patents and papers to a technology taxonomy is a crucial step to organize the massive knowledge and to discover innovative ideas. Patent examiners rely on the taxonomy to search for similar documents when granting or invalidating a patent application; attorneys use it to check whether the innovation points of an invention have been covered in previous literature; researchers use the taxonomy to monitor the technology trends in certain fields, and companies use it to outline the intellectual property landscape of its own or its competitors'.  The most commonly used taxonomies are International Patent Classification (IPC) \footnote{https://www.wipo.int/classifications/ipc/en/} and its newer version Cooperative Patent Classification (CPC) \footnote{https://www.epo.org/searching-for-patents/helpful-resources/first-time-here/classification/cpc.html}. Figure \ref{fig:ipc} illustrates the CPC hierarchy and the discriminative descriptions attached to each node.  

\begin{figure}
    \centering
    \includegraphics[width=0.48\textwidth]{ipc_1.png}
    \caption{Illustration of the CPC hierarchy.}
    \label{fig:ipc}
    \vspace{-0.5cm}
\end{figure}


The categorization is now mostly done manually by experts in patent offices. Due to the growing amount of patents and the limited number of domain experts, there has been an urge to automate the classification process. Also, as more and more technology innovations are published in patents, researchers with no background in patent classification may want to know which patents are most relevant to an academic paper. For this end, we aim to classify both patents and papers to the CPC \textit{subclass} with more than 600 labels.

Classifying patents and papers to CPC subclass is a challenging task because (1) there are a large number of labels that covers almost all technology domains, and the differences between labels are often subtle; (2) although mass amount of labelled data for patents is available, the annotated data for paper-to-CPC is very limited. Since labelling papers with CPC labels requires expert knowledge, large-scale human annotation is very expensive.

In this paper, we leverage the CPC label descriptions and use the Wide-and-Deep network to integrate label information with semantic information from input texts. We also construct a distant-supervised dataset for papers. \textbf{Our contributions} are:
\vspace{-0.2cm}
\begin{itemize}
	\item We prove the effectiveness of the label features through the Wide-and-Deep structure in CPC classification on more than 600 labels. 
	\item  We provide the state-of-the-art performance on classifying both patents and papers to a widely-used technology taxonomy.  Our model can serve as a simple and effective baseline for CPC classification tasks. 
\end{itemize}




% %The large number of target classes are attached with textual descriptions which are expected to guide the classification for especially boundary cases. 

% \textbf{Our contributions} are three-folded.
% \vspace{-0.2cm}
% \begin{itemize}
% 	\item We integrate label information in text classification through Wide \& Deep network. We prove the effectiveness of the label features on more than 600 classes. 
% 	\item We show that the proposed model can be easily adapted to another domain via limited amount of efforts on feature expansions. 
% 	\item  We provide the state-of-the-art performance on classifying both patents and papers to a patent taxonomy.  
% \end{itemize}
