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Abstract
We elucidate the landscape of companies that are embedded in artificial intelligence by examining the patent co-citation network of over 30000 patents published from 2015 through 2023. Our research highlights 10 prominent themes on which industry front-runners have invested their resources. The primary areas that emerged from topic modeling were speech, text processing, and image processing. While intuitive, it emphasizes the diversity of data that lends itself to various applications. The study has significant implications for both academics and practitioners. Researchers can enhance their understanding through topic modeling analysis, particularly by focusing on the distinct domains within AI. Our analysis, which clusters companies based on topics, offers a strategic perspective for organizations. It assists them in identifying areas with less competition and potential collaborations within their sphere. This study deepens our understanding of the current AI landscape.
Paper Number
tpp1334
Recommended Citation
Nair, Siddhi and Nerur, Sridhar, "Artificial Intelligence Landscape: Topic Modeling and Bibliometric Approach" (2024). AMCIS 2024 TREOs. 52.
https://aisel.aisnet.org/treos_amcis2024/52
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