Patent Analytics for Technological Innovation
Summary
Patent analytics integrates quantitative and qualitative examination of patent records to map the evolution, diffusion and impact of emerging technologies. By harnessing large-scale patent databases, practitioners distil indicators such as citation counts, filing trends and classification co-occurrences to gauge technological maturity, identify knowledge gaps and forecast innovation trajectories. Advanced methods—ranging from text mining and network analysis to machine learning and deep learning—enable the extraction of latent patterns within full-text descriptions, facilitating technology landscaping, competitive intelligence and strategic R&D planning. Globally, patent analytics informs policy decisions by revealing regional and sectoral strengths, supporting the allocation of public funding and guiding standardisation efforts. In industry, it underpins technology scouting and alliance formation, while in academia it enhances understanding of innovation dynamics across fields such as energy storage, telecommunications and biotechnology. By providing timely, data-driven insights into technological convergence, disruptive breakthroughs and market readiness, patent analytics serves as a cornerstone in shaping the innovation ecosystem and accelerating the translation of inventions into commercial and societal benefits.
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Patent Analytics for Technological Innovation publication trend
The graph below shows the total number of articles in patent analytics for technological innovation across all publications each year (not limited to Nature Index journals).
Technical terms
Patent analytics: The systematic analysis of patent documents to extract metrics and insights on technological development, diffusion and impact.
Text mining: Computational techniques to process and analyse large volumes of textual data, uncovering patterns and topics.
Machine learning: Algorithms that learn from data to make predictions or identify structures without explicit programming.
Generative adversarial network (GAN): A deep learning framework in which two neural networks compete to generate realistic synthetic data.
References
- Employing online big data and patent statistics to examine the relationship between end product's perceived quality and components' technological features. Technology in Society (2023).
- The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data. World Patent Information (2018).
- Forecasting emerging technologies using data augmentation and deep learning. Scientometrics (2020).
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