AI: Building the Better Mousetrap

Artificial intelligence may seem like an overly complex tool to bring to bear on something like redesigning a paper clip or mousetrap. However, the capabilities of modern AI make it well-suited for this type of product optimization challenge. At its core, AI excels at identifying patterns in data, generating novel ideas based on those patterns and iterating designs to meet specific performance objectives.

Let’s start with the paper clip. An AI system could ingest information on every paper clip design throughout history – the shapes, materials, coatings, manufacturing methods, as well as data on things like grip strength, tendency to rust or get bent out of shape and more. By analyzing this dataset, the AI could uncover the design traits that correlate to desired performance factors like longevity and gripping power. It could then use generative design techniques to create new paper clip concepts that combine the most effective elements in new ways, perhaps exploring novel materials or production methods along the way. Virtual simulations could then stress-test these AI-generated paper clip ideas before the best ones are prototyped in the real world.

For the mousetrap, the process would be similar – AI algorithms would learn from data on existing mousetrap designs, the mechanisms involved, the materials used, and metrics around factors like humaneness, ease of setting/re-setting, ability to avoid unintentional triggering and overall effectiveness at catching mice. With this knowledge, generative AI could start recombining the core “invention motifs” from successful mousetrap designs in novel permutations, perhaps combining the triggering mechanism from one design with the entrapment approach of another and the baiting strategy of a third. Physics simulations could then validate the viability and functionality of these AI-conceived mousetrap ideas.

In both cases, AI capabilities like machine learning, generative design, topology optimization, and simulation could be harnessed to reimagine seemingly perfected product designs in innovative new ways, all while optimizing for specific design objectives like improved performance, lower cost, or new functional advantages. While the end result may be as unassuming as an inexpensive strip of bent metal or a reusable piece of plastic for catching mice, the process of getting there could showcase critical AI capabilities with applications across industries. Sometimes rethinking the most mundane objects can lead to surprising innovations.