patent-analysis

You map a patent landscape by classifying patents with IPC/CPC taxonomies and summarizing activity by technology class to spot concentration and coverage gaps. This reveals where intellectual property is clustered and highlights whitespace opportunities.

Yes, you forecast technology emergence by fitting an S-curve or Bass diffusion model to your patent dataset. This identifies peak adoption timing and projects future technology diffusion rates for R&D portfolio benchmarking.

Citation networks measure patent value by building directed citation graphs and computing centrality signals like forward-citation influence. These network metrics act as proxies to identify the most impactful and foundational inventions within a dataset.

Yes, analyzing inventor collaboration requires Python dependencies including pandas, networkx, scipy, scikit-learn, and matplotlib. These libraries construct co-invention networks and estimate whitespace opportunity using recency and impact signals.

The best way to benchmark R&D portfolios is by comparing assignee and country activity across IPC-level patent landscapes. Combining classification summaries with citation network centrality provides a comparative view of technological influence and coverage.