Method to predict food color and recommend changes to achieve a target food color

Abstract

A color predictor is provided to predict the color of a food item given its formula comprising the ingredients and its quantities. The color predictor may utilize machine learning algorithms and a set of recipe data to train the color predictor. The color predictor can also be used by a color recommender to recommend changes in the given formula to achieve a target color.

What is claimed is: 
     
         1 . A method comprising:
 representing, in computer storage media, an initial formula for a food item as an initial formula vector;   representing, in computer storage media, target food color attributes for the food item as a target food color vector;   applying a neural network model to the initial formula vector and the target food color vector to determine a recommended formula for the food item, the recommended formula for the food item comprising changes to the initial formula for the food item to achieve the target food color attributes for the food item.   
     
     
         2 . The method of  claim 1 , wherein the initial formula vector comprises a set of ingredients and its respective quantities. 
     
     
         3 . The method of  claim 1 , wherein the neural network model implements at least one loss function to determine the changes to the initial formula, wherein the at least one loss function considers at least one of a plurality of factors including:
 an attribute distance between the initial formula and the target food color,   an ingredient distance between the initial formula and recommended formula,   an ingredient likelihood of the recommended formula to be a realistic formula, and   an ingredient sparsity of ingredients in the recommended formula.   
     
     
         4 . The method of  claim 1 , wherein the recommended formula is determined based on stochastic gradient descent (SGD) and back propagation models. 
     
     
         5 . The method of  claim 1 , wherein applying the neural network model comprises optimizing the initial formula vector using a sparsity loss function. 
     
     
         6 . The method of  claim 1 , wherein applying the neural network model comprises normalizing the initial formula vector prior being operated on by one or more of at least one loss function. 
     
     
         7 . The method of  claim 1 , wherein applying the neural network model further comprises:
 predicting, using a first machine learning model, color attributes associated with the initial formula, wherein the color attributes associated with the initial formula are represented as an initial formula color vector;   predicting, using a second machine learning model, color attributes associated with the recommended formula, wherein the color attributes associated with the recommended formula are represented as a recommended formula color vector.   
     
     
         8 . The method of  claim 7 , wherein the initial formula color vector and the recommended formula color vector are used by an attribute distance loss function to determine an attribute distance between the initial formula and the target food color. 
     
     
         9 . One or more non-transitory computer-readable storage media storing one or more instructions programmed which, when executed by one or more computing devices, cause:
 representing, in computer storage media, an initial formula for a food item as an initial formula vector;   representing, in computer storage media, target food color attributes for the food item as a target food color vector;   applying a neural network model to the initial formula vector and the target food color vector to determine a recommended formula for the food item, the recommended formula for the food item comprising changes to the initial formula for the food item to achieve the target food color attributes for the food item.   
     
     
         10 . The one or more non-transitory computer-readable storage media of  claim 9 , wherein the initial formula vector comprises a set of ingredients and its respective quantities. 
     
     
         11 . The one or more non-transitory computer-readable storage media of  claim 9 , wherein the neural network model implements at least one loss function to determine the changes to the initial formula, wherein the at least one loss function considers at least one of a plurality of factors including:
 an attribute distance between the initial formula and the target food color,   an ingredient distance between the initial formula and recommended formula,   an ingredient likelihood of the recommended formula to be a realistic formula, and   an ingredient sparsity of ingredients in the recommended formula.   
     
     
         12 . The one or more non-transitory computer-readable storage media of  claim 9 , wherein the recommended formula is determined based on stochastic gradient descent (SGD) and back propagation models. 
     
     
         13 . The one or more non-transitory computer-readable storage media of  claim 9 , wherein applying the neural network model comprises optimizing the initial formula vector using a sparsity loss function. 
     
     
         14 . The one or more non-transitory computer-readable storage media of  claim 9 , wherein applying the neural network model comprises normalizing the initial formula vector prior being operated on by one or more of at least one loss function. 
     
     
         15 . The one or more non-transitory computer-readable storage media of  claim 9 , wherein applying the neural network model further comprises:
 predicting, using a first machine learning model, color attributes associated with the initial formula, wherein the color attributes associated with the initial formula are represented as an initial formula color vector;   predicting, using a second machine learning model, color attributes associated with the recommended formula, wherein the color attributes associated with the recommended formula are represented as a recommended formula color vector.   
     
     
         16 . The one or more non-transitory computer-readable storage media of  claim 15 , wherein the initial formula color vector and the recommended formula color vector are used by an attribute distance loss function to determine an attribute distance between the initial formula and the target food color. 
     
     
         17 . A computing system comprising:
 one or more computer systems comprising one or more hardware processors and storage media; and   instructions stored in the storage media and which, when executed by the computing system, cause the computing system to perform:
 representing an initial formula for a food item as an initial formula vector; 
 representing target food color attributes for the food item as a target food color vector; 
 applying a neural network model to the initial formula vector and the target food color vector to determine a recommended formula for the food item, the recommended formula for the food item comprising changes to the initial formula for the food item to achieve the target food color attributes for the food item. 
   
     
     
         18 . The computing system of  claim 17 , wherein the initial formula vector comprises a set of ingredients and its respective quantities 
     
     
         19 . The computing system of  claim 17 , wherein the neural network model implements at least one loss function to determine the changes to the initial formula, wherein the at least one loss function considers at least one of a plurality of factors including:
 an attribute distance between the initial formula and the target food color,   an ingredient distance between the initial formula and recommended formula,   an ingredient likelihood of the recommended formula to be a realistic formula, and   an ingredient sparsity of ingredients in the recommended formula.   
     
     
         20 . The computing system of  claim 17 , wherein applying the neural network model further comprises:
 predicting, using a first machine learning model, color attributes associated with the initial formula, wherein the color attributes associated with the initial formula are represented as an initial formula color vector;   predicting, using a second machine learning model, color attributes associated with the recommended formula, wherein the color attributes associated with the recommended formula are represented as a recommended formula color vector.