Summary of Early Screening Of Potential Breakthrough Technologies with Enhanced Interpretability: a Patent-specific Hierarchical Attention Network Model, by Jaewoong Choi et al.
Early screening of potential breakthrough technologies with enhanced interpretability: A patent-specific hierarchical attention network model
by Jaewoong Choi, Janghyeok Yoon, Changyong Lee
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an interpretable machine learning approach for predicting future citation counts from patent texts using a patent-specific hierarchical attention network (PatentHAN) model. The approach utilizes a patent-specific pre-trained language model to capture the meanings of technical words in patent claims, a hierarchical network structure for detailed analysis at the claim level, and a claim-wise self-attention mechanism to reveal pivotal claims during the screening process. A case study of 35,376 pharmaceutical patents demonstrates the effectiveness of the approach in early screening of potential breakthrough technologies while ensuring interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using machine learning to help experts find important inventions in patent documents. It’s hard to understand why some inventions will be successful and others won’t just by looking at the text. This paper proposes a new way to use machine learning that helps explain how it arrives at its answers, which is useful for working with humans who need to make decisions about what inventions are most important. |
Keywords
» Artificial intelligence » Attention » Language model » Machine learning » Self attention
