Our work lays the foundation for a new product development process through human-AI co-creation. Our team builds AI-assisted creativity support tools that leverage market data, engineering simulations, and consumer preferences to empower designers and decision-makers to amplify their unique abilities. To support these activities, we also research new methods and data sources to capture human preferences and how new technology can best support creative behaviors.

Our team invents new technology to reduce the psychological distance between designers, decision-makers, and our customers to revolutionize how products come to market.

Our solutions amplify human users by providing new opportunities to enhance creativity, productivity, and quality of work.

Our customers are creatives, engineers, and decision-makers looking to accelerate the process of bringing innovative products to market.

In Future Product Innovation, we’re focused on three core questions: 

1. How can we inspire people to consider new solutions to increase organizational creativity?
2. How can we clarify the voice of the future customer to increase market acceptance?
3. How can we spread product development knowledge and constraints throughout the organization to increase efficiency?

These questions are core to the new product development roles throughout any company.

Our approach is enabled by two potentially revolutionary technologies. First, multi-modal generative models allow for the rapid generation of ideas for inspiration. This has the potential to revolutionize the creative work of many different forms. The question is how to focus on what’s being generated to meet the constraints of the designer, the physical world, and/or what people actually want in a product. Second, cognitive science theories combined with inexpensive behavioral sensors enable a deeper understanding of users than ever before. The question now is how to use this information to create new interfaces and interactions that amplify human abilities. 

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• Anticipatory Thinking in Design
• DesignAID: Using Generative AI and Semantic Diversity for Design Inspiration
• Weaving ML with Human Aesthetic Assessments to Augment Design Space Exploration: An Automotive Wheel Design Case Study
• Semantic properties of word prompts shape design outcomes: understanding the influence of semantic richness and similarity
• Bridging Design Gaps: A Parametric Data Completion Approach With Graph-Guided Diffusion Models
• Understanding the Cognitive Complexity in Language Elicited Product Images