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LLMs and Transformers Enhance Patent Innovation Analysis Pipeline

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel patent analytics pipeline that leverages large language models (LLMs) and transformer representations to classify and map patent innovation. This system uses multiple LLMs to independently categorize patents by novelty and innovation type, employing a consensus mechanism for labeling. The pipeline also clusters PatentSBERTa embeddings to discover topics, applying corpus-adaptive techniques and a patent-specific stopword list for improved representation. Finally, an LLM labels these topics based on keywords and representative patents, ensuring that all model decisions and evidence are retained for auditability. AI

IMPACT This pipeline offers a traceable and auditable method for LLM-assisted patent analysis, potentially improving technology intelligence.

RANK_REASON The item is an academic paper detailing a new methodology for patent analysis using LLMs. [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 →

LLMs and Transformers Enhance Patent Innovation Analysis Pipeline

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The item is an academic paper detailing a new methodology for patent analysis using LLMs. [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) · C. Okan Sakar ·

    Large Language Model Consensus and Corpus-Adaptive Topic Modeling for Classifying and Mapping Patent Innovation

    Patent analytics increasingly relies on transformer representations and large language models (LLMs), yet linking conceptual classification, topic formation and interpretation requires explicit decisions about model roles and the evidence retained at each stage. In this design-or…

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