arXiv:2511.10657v2 Announce Type: replace-cross Abstract: We study self-supervised patent representation learning with contrastive objectives. A standard baseline constructs positives by encoding the same text twice under independent dropout masks, but applying this recipe to long, structured patent documents requires careful calibration. We show that dropout-only training can be substantially strengthened by tuning temperature and dropout rate, yet its best configuration is evaluation-dependent and does not transfer uniformly from title--abstract retrieval to claim-to-disclosure retrieval. We
The proliferation of complex, long-form data like patent documents necessitates advanced AI techniques for efficient processing and knowledge extraction, making self-supervised learning critical for scaling patent analysis.
Improving patent representation learning can significantly enhance innovation tracking, competitive intelligence, and intellectual property (IP) management for businesses and governments.
The ability to accurately and efficiently process large patent corpuses has improved, leading to more robust retrieval and analysis systems, though challenges remain in deployment.
- · LegalTech (Patent Analysis)
- · R&D intensive industries
- · Generative AI companies
- · Intellectual Property firms
- · Manual patent examiners
- · Companies with weak IP strategies
More accurate and efficient retrieval of patent information, aiding in novel invention discovery and infringement detection.
Accelerated innovation cycles due to improved access to prior art and reduced time spent on patent searches.
Potential for sovereign entities to more effectively manage national innovation landscapes and protect domestic intellectual property.
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Read at arXiv cs.LG