Assessment of Pharmaceutical Patent Novelty with Siamese Neural Networks

Heba El-Shimy*, Hind Zantout, Hani Ragab Hassen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
23 Downloads (Pure)


Patents in the pharmaceutical field fulfil an important role as they contain details of the final product that is the culmination of years of research and possibly millions of dollars of investment. It is crucial that both patent producers and consumers are able to assess the novelty of such patents and perform basic processing on them. In this work, we review approaches in the literature in patent analysis and novelty assessment that range from basic digitisation to deep learning-based approaches including natural language processing, image processing and chemical structure extraction. We propose a system that automates the process of patent novelty assessment using Siamese neural networks for similarity detection. Our system showed promising results and has a potential to improve upon the current patent analysis methods, specifically in the pharmaceutical field, by not just focusing on the task from a Natural Language Processing perspective, but also, adding image analysis and adaptations for chemical structure extraction.

Original languageEnglish
Title of host publicationArtificial Neural Networks in Pattern Recognition. ANNPR 2022
EditorsNeamat El Gayar, Edmondo Trentin, Mirco Ravanelli, Hazem Abbas
Number of pages16
ISBN (Electronic)9783031206504
ISBN (Print)9783031206498
Publication statusPublished - 2023
Event10th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition 2022 - Dubai, United Arab Emirates
Duration: 24 Nov 202226 Nov 2022

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition 2022
Abbreviated titleANNPR 2022
Country/TerritoryUnited Arab Emirates
Internet address


  • Chemical structure extraction
  • CNN
  • Document analysis
  • LSTM
  • Optical character recognition
  • Pharmaceutical patents
  • Siamese neural networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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