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New neural parser generates invention graphs for patent search

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel neural parser that adapts biaffine attention to directly predict invention graphs from patent text, overcoming limitations of traditional rule-based parsers. This approach, trained on distilled data from one million rule-parsed documents, reduces complexity and allows for processing of documents exceeding 40,000 tokens without retraining. The neural graphs generated by this system improve citation recall by up to 1.1% in downstream retrieval tasks, while also lowering inference costs. AI

IMPACT This new method for generating invention graphs could improve the accuracy and efficiency of patent prior art searches.

RANK_REASON This is a research paper detailing a new method for structured prediction in information retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New neural parser generates invention graphs for patent search

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This is a research paper detailing a new method for structured prediction in information retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Sebastian Björkqvist ·

    From Rules to Neural Graphs: Scalable Structured Prediction for Patent Prior Art Search

    Patent search requires processing documents routinely exceeding tens of thousands of tokens. Most neural retrieval approaches operate on truncated inputs, limiting their effectiveness. Graph-based retrieval addresses this by representing each patent as a structured invention grap…

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