Entity recognition and hierarchical modeling improve cross-document claim matching for more accurate novelty and inventive-step assessment.
Entity matching with hierarchical dependency analysis reveals cross-document correlations that improve patentability and validity assessment.
Automated keyword extraction, related-term weighting, and ranking improve intellectual property document search accuracy with simple input.
An intermediary LLM API extracts special technical features from patent documents to generate annotations, tables, summaries, and claim drafts with lower integration burden.
Pre-extracted biological sequences linked to attribute data enable faster, more precise patent document retrieval for novelty and infringement searches.
External LLM mediation combines search-based examination records with local processing to generate reliable refusal forecasts and argument drafts.