Strategic forecasting of internet of things technologies through patent social network and innovation cluster analysis
Mehrdad Maghsoudi, Reza Nourbakhsh, Mehrdad Agha Mohammadali Kermani, Rahim Khanizad

TL;DR
This paper presents a comprehensive forecasting framework for IoT technologies by integrating patent social network analysis, advanced text mining, and life cycle modeling to identify innovation clusters and collaboration dynamics.
Contribution
It introduces a unified methodological approach combining BERT embeddings, clustering, life cycle, and community detection to analyze large-scale IoT patent data.
Findings
Identified nine distinct IoT technology clusters.
Most clusters will reach saturation between 2023 and 2027.
Revealed divergent strategic orientations among collaborative communities.
Abstract
The rapid proliferation of Internet of Things (IoT) technologies necessitates robust forecasting mechanisms to guide strategic decision-making amid increasingly complex innovation landscapes. Despite extensive research employing patent analysis for technology forecasting, existing studies lack systematic integration of social network analysis, advanced text mining, and life cycle modeling to comprehensively map IoT technological evolution and collaborative dynamics. This study addresses these gaps by analyzing 154,227 IoT-related patents through a unified methodological framework combining BERT-based text embeddings, k-means clustering with Davies-Bouldin optimization, S-curve life cycle modeling, and Louvain community detection. The analysis identified nine distinct technology clusters spanning foundational infrastructure (Smart Monitoring and Sensor Systems, Network Communication and…
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Taxonomy
TopicsBig Data and Digital Economy · Smart Systems and Machine Learning · Intellectual Property and Patents
