Product forms in quantitative design methods are typically expressed with a mathematical representation such as vectors, trees, graphs, and grammars. Such formal representations are restrictive in terms of realism or flexibility, and this limits their utility for human designers who typically create product forms in a design space that is restricted by the medium (e.g., free-hand sketching) and by their cognitive skills (e.g., creativity and experience). To increase the value of formal representations to human designers, this paper proposes to represent the design space as designs sampled from a statistical distribution of form and estimate a generative model of this distribution using a large set of images and design attributes of previous designs. This statistical representation approach is both flexible and realistic, and is estimated using a deep (multi-layer) generative model. The value of the representation is demonstrated in a study of two-dimensional automobile body forms. Using 180,000 form data of automobile designs over the past decade, we can morph a vehicle form into different body types and brands, thus offering human designers potential insights on realistic new design possibilities.

Designers faced with the task of developing a new product model of a brand must balance several considerations. The design must be novel and express attributes important to the customers, while also recognizable as a representative of the brand. This balancing is left to the intuition of the designers, who must anticipate how customers will perceive the new design. Oftentimes, the design freedom used to meet a product attribute can compromise the recognition of the product as a member of the brand. In this paper, an experiment is conducted for measuring changes in ten styling attributes common to both design freedom and brand recognition for automotive designs from four brands, Audi, BMW, Cadillac, and Lexus, using customer responses to two- and three-dimensional vehicle designs created and presented interactively through a crowdsourced web application. Results show that while brand recognition is highly dependent on the manufacturer, two brands have strong negative relationship between design freedom and brand recognition, suggesting that these two manufacturers face a significant challenge when evolving their respective brand styling. This study is a first effort toward quantifying and predicting tradeoffs between design freedom and brand recognition, contributing to existing efforts that augment human intuition during strategic design decisions.

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Design preference models are used widely in product planning and design development. Their prediction accuracy requires large amounts of personal user data including purchase and other personal choice records. With increased Internet and smart device use, sources of personal data are becoming more varied and their capture more ubiquitous. This situation leads to questioning whether there is a trade off between improving products and compromising individual user privacy. To advance this conversation, we analyze how privacy safeguards may affect design preference modeling. We conduct an experiment using real user data to study the performance of design preference models under different levels of privacy. Results indicate there is a tradeoff between accuracy and privacy. However, with enough data, models with privacy safeguards can still be sufficiently accurate to answer population-level design questions.