Improving Patent Mining and Relevance Classification using Transformers
This work addresses the problem of patent overload for companies, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of automating patent filtering to reduce the time and cost of patent analysis for companies, achieving a reduction in workload while maintaining recall and precision metrics.
Patent analysis and mining are time-consuming and costly processes for companies, but nevertheless essential if they are willing to remain competitive. To face the overload induced by numerous patents, the idea is to automatically filter them, bringing only few to read to experts. This paper reports a successful application of fine-tuning and retraining on pre-trained deep Natural Language Processing models on patent classification. The solution that we propose combines several state-of-the-art treatments to achieve our goal - decrease the workload while preserving recall and precision metrics.