DS 2 PT: A Deep Two-Stage Patent Text Segmentation Framework Informed by Low-Latency Neural Network Characteristics

PT effectively captures hierarchical contextual information across two stages, significantly improving segmentation accuracy without semantic loss. Furthermore, this research draws inspiration from advancements in cross-lingual speech-to-text systems with low-latency neural networks for real-time applications. While the domains differ, the core technical challenges are analogous: both require models to process sequential, information-dense input (audio streams or long sentences) into structured, meaningful units (transcribed text or segmented clauses) with high accuracy and efficiency. The principles of low-latency neural networks-such as efficient context modeling, parallelizable architectures, and real-time incremental processing-inform the design of our segmentation pipeline to enhance its scalability and potential for integration into real-time patent analysis systems. Similarly, the cross-lingual capability highlights the importance of model generalization, which aligns with our goal of developing a domain-adaptive segmentation tool for diverse patent corpora.