A survey on automated and AI-based tools for patent retrieval with a special focus on the life sciences domain

A survey on automated and AI-based tools for patent retrieval with a special focus on the life sciences domain

Journal article

1 volume.

In the knowledge-driven economy, identifying high-potential inventions immediately upon disclosure is critical for strategic R&D management. Traditional valuation methods predominantly rely on forward citations, which suffer from “time-lag biases,” rendering them ineffective for assessing newly published patents. Furthermore, conventional AI-based approaches often treat patents as isolated textual entities, overlooking their latent topological relationships. This study proposes a “zero-lag” framework for early-stage patent valuation by introducing Strategic Topological Value (STV)—a metric that quantifies an invention's relational significance and structural importance within a technological network.

Crossref

World Patent Information ; 85, June, 2026

World Patent Information

Oxford [England] : Elsevier Ltd., 2026.

eng

https://www.sciencedirect.com/science/article/abs/pii/S0172219023000108

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