California Patent of the Month – May 2021
Formulations are constantly changing – ingredients could become unavailable or increase in price beyond a reasonable amount. When that happens, alternative ingredients or formulas are tested to try and achieve the same or better results. But, reformulating can be a lengthy and costly process as many trials are usually needed. Citrine Informatics, Inc. have developed a machine learning system which can evaluate formulations beforehand, improving the speed and effectiveness of the testing process.
The system models performance of formulations for a recipe that now includes previously untested ingredients. This helps to show how that new ingredient might interact with the rest of the ingredients, and highlight if there are any unforeseen problems. Their system has been trained with historical data to create a large knowledge bank of ingredient properties and interactions. The user would input information such as the target performance of the formulation, any constraints, and the formulation in use. The machine can then return the newly tested formulations predicted properties and the likelihood of meeting the target properties. Based on this information, the user can then decide if moving that test formulation to the next level is worthwhile, or if other variations should be examined. This system can reduce time needed in R&D as well as prevent costly testing with ingredients that won’t serve the intended purposes.
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