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From LLMs to AI agents: a systematic benchmark for SAO structure extraction in patent analytics.

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Gemini 1.5 Pro agents significantly improve patent analysis by extracting Subject-Action-Object (SAO) relationships. This agent-based approach enhances accuracy in collaborative robotics patent data, outperforming other models.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Robotics

Background:

  • The exponential growth in patent literature necessitates advanced automated methods for information extraction.
  • Extracting Subject-Action-Object (SAO) relationships is crucial for understanding technological innovation within patents.
  • Existing methods struggle with the complexity and volume of patent data, especially in specialized domains like collaborative robotics.

Purpose of the Study:

  • To develop and evaluate an information extraction benchmark framework for the collaborative robotics domain.
  • To compare the performance of large language models (LLMs) including GPT-4o, Gemini 1.5 Pro, and Claude 3.5 Sonnet, with and without agent architectures.
  • To assess the impact of agent frameworks on the accuracy and efficiency of SAO relationship extraction from patent literature.

Main Methods:

  • Construction of a specialized information extraction benchmark for collaborative robotics patents.
  • Implementation of a closed-loop "reflection and refinement" model incorporating thought chain and ReAct strategies.
  • Comparative analysis of three LLMs (GPT-4o, Gemini 1.5 Pro, Claude 3.5 Sonnet) and their agent-based counterparts, with parameter tuning for factual accuracy.
  • Statistical significance testing using a t-test on 501 independent samples.

Main Results:

  • Agent architectures positively impacted the performance of all tested LLMs.
  • The Gemini 1.5 Pro agent achieved the highest performance with an F1 score of 89.58% and a BERTScore of 87.73%.
  • Agent-based models significantly outperformed baseline LLMs, especially in parsing complex nested clauses and filtering legal jargon, despite increased computational cost (40-60x).

Conclusions:

  • Agent architectures provide a significant performance boost for SAO relationship extraction in patent analysis.
  • Gemini 1.5 Pro, when utilized with an agent framework, demonstrates superior capability in handling complex patent data within the collaborative robotics domain.
  • The study offers a methodological framework and empirical evidence for deploying agent-based LLMs in specialized technical domains, justifying the computational overhead for enhanced recall and risk mitigation.