## 2 Literature Review  
Document relevance filtering has long been a challenge due to the sheer volume of unstructured text data and the ambiguity in terminology. Traditional approaches, such as semantic searches using keywords, rely on exact or partial matches and often return a mix of relevant and irrelevant results, particularly in multidisciplinary fields [3]. Patent documents, in particular, frequently use broad non-standardized language to maximize claim scope, further complicating relevance filtering [4]. In a recent review of 78 articles, [PERSON] et al. (2024) pointed out many of the gaps remaining in the field of document relevance filtering and concluded that despite advancements so far, the technical nature of documents, such as those employing patent lingo, increases the difficulty of retrieving high-relevance documents [2].  
Recent advances in NLP and ML have introduced more sophisticated methods for addressing these challenges [5]. NLP techniques have gained traction in processing patent text [6], with topic modeling techniques such as LDA and NMF providing a probabilistic framework to uncover latent themes in text corpora and serve as a method of topic filtering [7]. However, limitations in their ability to align topics with document relevance highlight the need for improved feature engineering and domain-specific adjustments [8].  
Researchers have widely adopted SML techniques like gradient boosting (GB), random forests (RFs), and support vector machines (SVMs) for various applications of text processing [9]. However, only a few studies have recently adopted SML methods for patent retrieval and relevance classification [10]. D-ANNs have also emerged as powerful tools to mine high-dimensional text data, leveraging their ability to learn nonlinear and hierarchical feature representations [11]. However, D-ANNs have high computational demands [12].  
Data visualization methods like word clouds and term co-occurrence networks facilitate exploratory analysis, enabling researchers to identify patterns and relationships in large datasets [13]. While promising, their effectiveness heavily depends on feature selection and the quality of input data [14]. The current study proposed applying t-SNE, a dimensionality reduction technique, coupled with clustering algorithms like k-means, to help visualize and segregate text data.  
The application of ML techniques in patent analysis remains nascent but increasingly critical [15]. Patents are a rich source of technological insights, and efficiently filtering relevant documents can significantly enhance innovation forecasting and strategic planning. Yet, there is a lack of recent studies focused on patent document relevance filtering, as evidenced by the few documents retrieved in this literature review. The studies that focused on transportation technologies, such as solid-state batteries and autonomous vehicle systems, highlight the need for tailored ML solutions that account for domain-specific nuances and evolving terminologies. By building on this foundation, the current study seeks to bridge gaps in the existing literature and provide a benchmark for relevance filtering in multidisciplinary patent datasets.