KAPPA: A Generic Patent Analysis Framework with Keyphrase-Based Portraits

TL;DR

KAPPA is a comprehensive framework that constructs keyphrase-based patent portraits using advanced language models, significantly improving patent analysis accuracy and efficiency by capturing domain-specific knowledge.

Contribution

The paper introduces a novel semantic-calibrated keyphrase generation paradigm and a portrait-based analysis framework, advancing patent analysis through interpretable representations.

Findings

Significant improvements over state-of-the-art keyphrase generation models.

Effective capture of domain-specific knowledge in patent portraits.

Enhanced semantic representation for patent analysis tasks.

Abstract

Patent analysis highly relies on concise and interpretable document representations, referred to as patent portraits. Keyphrases, both present and absent, are ideal candidates for patent portraits due to their brevity, representativeness, and clarity. In this paper, we introduce KAPPA, an integrated framework designed to construct keyphrase-based patent portraits and enhance patent analysis. KAPPA operates in two phases: patent portrait construction and portrait-based analysis. To ensure effective portrait construction, we propose a semantic-calibrated keyphrase generation paradigm that integrates pre-trained language models with a prompt-based hierarchical decoding strategy to leverage the multi-level structural characteristics of patents. For portrait-based analysis, we develop a comprehensive framework that employs keyphrase-based patent portraits to enable efficient and accurate patent analysis. Extensive experiments on benchmark datasets of keyphrase generation, the proposed model achieves significant improvements compared to state-of-the-art baselines. Further experiments conducted on real-world patent applications demonstrate that our keyphrase-based portraits effectively capture domain-specific knowledge and enrich semantic representation for patent analysis tasks.