Estee Lauder
IBM developed an algorithm that studies existing fragrance formulas and then compares the ingredients to other data sets, like geography and customer age. This algorithm, which was created in IBM’s Thomas J. Watson Research Center and which the company has named Philyra, can now develop new perfumes that will target very specific market segments.
Role
Project Manager
Responsibilities
Resource planning, budget
AI for Product Composition
Building on previous IBM research using AI to pair flavors and for recipe creation, as well as our new IBM Research AI for Product Composition, we created Philyra. It is a system that uses new and advanced machine learning algorithms to sift through hundreds of thousands of formulas and thousands of raw materials, helping identify patterns and novel combinations. Philyra does more than serve up inspiration – it can design entirely new fragrance formulas by exploring the entire landscape of fragrance combinations to discover the whitespaces in the global fragrance market.
Discovering New Ingredient Combinations with Machine Learning
The algorithms that learn and predict:
alternative raw material complements and substitutes that could be used in a formula;
the appropriate dosing for a raw material based on usage patterns;
the human response (pleasantness and gender appropriateness); and
the novelty of the fragrance by comparing it to a large set of commercially available fragrances.
When it comes to new perfume design, novelty is a major driver, and Philyra learns a distance model to identify fragrances that are close in smell to existing fragrances. The larger the distance between a fragrance and its neighbors, the more novel the perfume is predicted to be.
