Abstract: Example food processing systems and methods are described. In one implementation, a system includes a molecular embedder that receives a molecular profile of a single-molecule ingredient from multiple single-molecule ingredients and generates a representation of the single-molecule ingredient. A preparation modeler receives representations of single-molecule ingredients and preparation instructions. The molecular embedder also generates a representation of the prepared ingredients. A predictor receives a representation of the prepared ingredients and generates predicted characteristics of the prepared ingredients.

Abstract: Example molecular embedding systems and methods are described. In one implementation, a system includes a molecular embedder configured to receive structural and chemical information associated with a single-molecule ingredient from a plurality of single-molecule ingredients. The molecular embedder also generates a representation of the single-molecule ingredient. A preparation modeler receives multiple representations of single-molecule ingredients and preparation instructions. The molecular embedder generates a representation of the prepared ingredients. A predictor receives a representation of the prepared ingredients and generates predicted characteristics of the prepared ingredients.

Abstract: Example mixture modeling systems and methods are described. In one implementation, a system includes an encoder that receives multiple base ingredients and produces multiple corresponding representations. A composite modeler receives a mixture definition comprising a list of base ingredients and their relative proportions. The composite modeler generates a representation of the mixture. A decoder is receives a representation of a mixture and generates a list of features.

Abstract: Example virtual tasting systems and methods are described. In one implementation, a virtual tasting system identifies multiple food products, multiple tasters, and multiple questions. The virtual tasting system then identifies a full set of quadruplets of the form: first food product, second food product, taster, question. A subset of quadruplets from the full set of quadruplets are then identified. The virtual tasting system then measures a value associated with each quadruplet from the subset of quadruplets. The virtual tasting system then outputs estimated values associated with every triplet in a full set of triplets of the form: food product, taster, question.

Abstract: The disclosure relates to systems, methods and programs implicitly determines the individual gaze correction required for each user when it comes to determining the point-of-regard and region of interest using a seamless-adaptation-process (SAP) in settings when calibration is not practical/desired (e.g. long-range display eye sensing). The present systems, methods, and programs also correct for variables arising from salient features such as user eye attributes (e.g., ocular dominance or chronic eyesight conditions) that are not visible within a digital frame, environmental conditions, and camera-screen settings. The method is not eye-tracking method dependent, can perform under all environmental conditions while the displayed content is known. The concept of a data driven seamless gaze correction can be extended beyond long-range displays to any gaze-enabled device where fixation accuracy can be improved (e.g., mobile phones, tablets etc.).