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“Unravelling technology meta-landscapes: A patent analytics approach to assess trajectories and fragmentation” focuses on extracting insights from patent landscapes and presents a graphical representation of time-evolved ranked Pareto distribution of patent family counts per assignee using power law analysis. The authors employ a method termed Discrete Pareto Analysis (DPA) to characterize the distribution of resources (patent families) among players (assignees) in various technology domains. The study addresses data-analytical issues related to name harmonization, regression depth, and IPC classification, presenting insights into the trajectories and consolidation of technology landscapes.
In 2021, around 3.4 million patent applications were published, and the authors anticipate this number to double in the next 15 years. The article underscores the commercial activity in patent analytics, facilitated by modern data science. The methodology involves analyzing patent landscapes based on IPC codes, employing DPA to extract metrics, and addressing challenges like name harmonization and regression depth. The results are presented through visual representations, highlighting the growth rates and trajectories of various IPC subclasses. The study also discusses the impact of using different patent family definitions, such as simple patent families versus international patent families (IPFs), on technology trajectory analysis.
The article emphasizes the importance of understanding industry dynamics through patent landscape analysis, providing insights into technology trends, consolidation, and fragmentation. The authors validate their methodology through case studies in industries such as wind motors, self-driving vehicles, and typewriters. The meta-landscapes presented in the paper serve as a valuable tool for predicting industry dynamics, with patent activity often preceding market growth.
The study specifically highlights the significance of LexisNexis® PatentSight® in addressing challenges related to name harmonization. It emphasizes that PatentSight uses the Ultimate Owner concept, considering the corporate structure of a company and accounting for reassignments, mergers, and acquisitions. The platform’s name harmonization processes are deemed essential for accurate patent landscape analysis, given the lack of standardized formats globally for assignee names across patent databases.
The discussion section addresses method selection considerations, the impact of name harmonization on accuracy, and the choice between simple patent families and IPFs for analysis. The authors conclude by highlighting the uniqueness of each landscape trajectory and the potential for interpreting industry dynamics and future trends through patent analytics.
Future work is suggested in investigating discrepancies between predicted and observed patent family counts for top-ranked owners, exploring the diversification strategy of companies based on the distribution of patent families between IPC codes, and further differentiation between logical and owner fragmentation within a company’s portfolio.
The article provides a comprehensive exploration of patent analytics as a tool for understanding technology meta-landscapes, offering valuable insights for executives, public servants, and individuals interested in industry trends and trajectories.
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