Neural network method of generating food formulas

Abstract

Techniques to mimic a target food item using artificial intelligence are disclosed. A formula generator is trained using combinations of ingredients. A training set may include, for each combination of ingredients, proportions, and features of the ingredients in a respective combination of ingredients. Given a target food item, the formula generator determines a predicted formula that matches the given target food item. The predicted formula includes a set ingredients and a respective proportion of each ingredient in the set of ingredient.

1 . A method comprising:
 for each combination of a plurality of combinations of ingredients, representing a respective combination as a digitally stored ingredients vector, and representing features of the respective combination as a digitally stored feature vector;   training a neural network based on a plurality of digitally stored feature vectors and a plurality of digitally stored ingredients vectors associated with the combinations of ingredients to learn a trained neural network;   representing features of a target food item as a digitally stored feature vector;   using the trained neural network to predict a formula for a target food item, the formula comprising a set of ingredients and proportions thereof.   
     
     
         2 . The method of  claim 1 , wherein each digitally stored feature vector represents a set of features including at least one chemical feature, nutritional feature, and molecular feature, and wherein each digitally stored ingredients vector includes a proportion of each ingredient in a corresponding combination of ingredients. 
     
     
         3 . The method of  claim 1 , wherein a digitally stored feature vector of the plurality of digitally stored feature vectors and a digitally stored ingredients vector from the plurality of digitally stored ingredients vectors are matched. 
     
     
         4 . The method of  claim 1 , wherein the trained neural network is applied in reverse to predict the formula of the target food item. 
     
     
         5 . The method of  claim 1 , further comprising refining the predicted formula to reduce a total number of ingredients in the set of ingredients and to rebalance proportions of remaining ingredients in the set of ingredients. 
     
     
         6 . The method of  claim 1 , wherein the trained neural network comprises N hidden layers, wherein the N hidden layers include a first hidden layer and one or more subsequent hidden layers after the first hidden layer, wherein a size of each subsequent hidden layer is a factor of a size of its previous layer, wherein N is dependent on a dimension size of input data of the neural network and a ratio of the dimension size of input data and a dimension size of output data of the neural network. 
     
     
         7 . The method of  claim 6 , wherein the size of each subsequent hidden layer is either double or half a size of its previous layer. 
     
     
         8 . One or more non-transitory computer-readable storage media storing one or more instructions programmed, when executed by one or more computing devices, cause:
 for each combination of a plurality of combinations of ingredients, representing a respective combination as a digitally stored ingredients vector, and representing features of the respective combination as a digitally stored feature vector;   training a neural network based on a plurality of digitally stored feature vectors and a plurality of digitally stored ingredients vectors associated with the combinations of ingredients to learn a trained neural network;   representing features of a target food item as a digitally stored feature vector;   using the trained neural network to predict a formula for a target food item, the formula comprising a set of ingredients and proportions thereof.   
     
     
         9 . The one or more non-transitory computer-readable storage media of  claim 8 , wherein each digitally stored feature vector represents a set of features including at least one chemical feature, nutritional feature, and molecular feature, and wherein each digitally stored ingredients vector includes a proportion of each ingredient in a corresponding combination of ingredients. 
     
     
         10 . The one or more non-transitory computer-readable storage media of  claim 8 , wherein a digitally stored feature vector of the plurality of digitally stored feature vectors and a digitally stored ingredients vector from the plurality of digitally stored ingredients vectors are matched. 
     
     
         11 . The one or more non-transitory computer-readable storage media of  claim 8 , wherein the trained neural network is applied in reverse to predict the formula of the target food item. 
     
     
         12 . The one or more non-transitory computer-readable storage media of  claim 8 , wherein the one or more instructions, when executed by the one or more computing devices, further cause refining the predicted formula to reduce a total number of ingredients in the set of ingredients and to rebalance proportions of remaining ingredients in the set of ingredients. 
     
     
         13 . The one or more non-transitory computer-readable storage media of  claim 8 , wherein the trained neural network comprises N hidden layers, wherein the N hidden layers include a first hidden layer and one or more subsequent hidden layers after the first hidden layer, wherein a size of each subsequent hidden layer is a factor of a size of its previous layer, wherein N is dependent on a dimension size of input data of the neural network and a ratio of the dimension size of input data and a dimension size of output data of the neural network. 
     
     
         14 . The one or more non-transitory computer-readable storage media of  claim 13 , wherein the size of each subsequent hidden layer is either double or half a size of its previous layer. 
     
     
         15 . 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 features of a target food item as a digitally stored feature vector; 
 using a neural network to predict a formula for a target food item, the formula comprising a set of ingredients and proportions thereof; 
 wherein the neural network is trained by:
 for each combination of a plurality of combinations of ingredients,
 representing a respective combination as a digitally stored ingredients vector, and 
 representing features of the respective combination as a digitally stored feature vector; 
 training the neural network based on a plurality of digitally stored feature vectors and a plurality of digitally stored ingredients vectors associated with the combinations of ingredients to learn a trained neural network; 
 
 
 wherein the trained neural network comprises N hidden layers, wherein the N hidden layers include a first hidden layer and one or more subsequent hidden layers after the first hidden layer, wherein a size of each subsequent hidden layer is a factor of a size of its previous layer. 
   
     
     
         16 . The computing system of  claim 15 , wherein each digitally stored feature vector represents a set of features including at least one chemical feature, nutritional feature, and molecular feature, and wherein each digitally stored ingredients vector includes a proportion of each ingredient in a corresponding combination of ingredients. 
     
     
         17 . The computing system of  claim 15 , wherein a digitally stored feature vector of the plurality of digitally stored feature vectors and a digitally stored ingredients vector from the plurality of digitally stored ingredients vectors are matched. 
     
     
         18 . The computing system of  claim 15 , wherein the trained neural network is applied in reverse to predict the formula of the target food item. 
     
     
         19 . The computing system of  claim 15 , further comprising refining the predicted formula to reduce a total number of ingredients in the set of ingredients and to rebalance proportions of remaining ingredients in the set of ingredients. 
     
     
         20 . The computing system of  claim 15 , wherein the trained neural network comprises N hidden layers, wherein the N hidden layers include a first hidden layer and one or more subsequent hidden layers after the first hidden layer, wherein a size of each subsequent hidden layer is a factor of a size of its previous layer, wherein N is dependent on a dimension size of input data of the neural network and a ratio of the dimension size of input data and a dimension size of output data of the neural network.