Inside the Collective Mind: Features Extraction to Support Automated Design Space Explorations

Conference paper


The paper investigates the possibility to extract meaningful information out of natural language design conversation. This meaningful information, referred in this paper as features, represents possible design changes and solutions discussed during collaborative design sessions. Without relying on user input and without disrupting the natural course of the conversations, we envision an automatic implementation of these changes and solutions into a parametric model. The aim of such a system is to allow for an automatic design space exploration without interrupting the design sessions. In this direction, the paper employs mixed research methods which make use of quantitative and qualitative analysis. The results obtained indicate the possibility of extracting structured information to perform changes in various parametric models automatically. The paper also provides discussions around specific limitations, such as unclear precedents due to multimodality input.


Natural language Collaborative design Design automation Design meetings 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Civil Systems EngineeringInstitute of Civil Engineering, Technical University of BerlinBerlinGermany

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