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Updated: May 9, 2026

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DS2PT: A Deep Two-Stage Patent Text Segmentation Framework Informed by Low-Latency Neural Network Characteristics.

Boting Geng1, Hongxia Wang1, Pengliang Zhang1

  • 1School of Computer Science and Technology, Zhejiang University of Water Resources and Electric Power, Hangzhou, China.

Big Data
|May 8, 2026
PubMed
Summary
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This study introduces a Deep Segmentation Model for Patent Text (DS²PT) to break down complex patent sentences into shorter, meaningful units. This AI-driven approach enhances patent analysis and search accuracy.

Area of Science:

  • Computational Linguistics
  • Artificial Intelligence
  • Data Mining

Background:

  • Patent text segmentation is crucial for patent analysis and search.
  • Traditional methods are labor-intensive and lack generalizability.
  • Complex patent sentences hinder downstream processing.

Purpose of the Study:

  • To develop an automated, accurate method for patent text segmentation.
  • To improve the efficiency and generalizability of patent data mining.

Main Methods:

  • A two-stage framework, Deep Segmentation Model for Patent Text (DS²PT), was proposed.
  • Stage 1: Coarse segmentation using a conditional random field model.
  • Stage 2: Deep, context-aware segmentation using the ALBERT model.
Keywords:
ALBERTcross-lingual processinglow-latency neural networkspatent analysispatent text segmentationreal-time systems

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Main Results:

  • DS²PT significantly improves segmentation accuracy without semantic loss.
  • The model effectively captures hierarchical contextual information.
  • The approach shows potential for real-time patent analysis systems.

Conclusions:

  • DS²PT offers a robust solution for patent text segmentation.
  • The model's design is inspired by low-latency neural networks for efficiency.
  • This work facilitates domain-adaptive patent data processing.