System and method for sample evaluation

Abstract

In variants, a method for analog product determination can include: determining functional property feature values for a target and determining variable values for a prototype based on the functional property feature values for the target.

We claim: 
     
         1 . A method, comprising:
 measuring a target functional property signal for a target sample;   extracting target functional property feature values from the target functional property signal; and   using a trained model, determining a set of manufacturing variable values based on the target functional property feature values.   
     
     
         2 . The method of  claim 1 , wherein the prototype functional property signal and the target functional property signal each comprise a data time series. 
     
     
         3 . The method of  claim 2 , wherein the prototype functional property feature values and target functional property feature values are each extracted using time series decomposition. 
     
     
         4 . The method of  claim 1 , wherein the functional property features comprise non-semantic features. 
     
     
         5 . The method of  claim 4 , wherein the functional property features further comprise semantic features. 
     
     
         6 . The method of  claim 1 , wherein the comparison between the prototype functional property feature values and target functional property feature values comprises a distance between the prototype functional property feature values and the target functional property feature values. 
     
     
         7 . The method of  claim 6 , further comprising weighting the prototype functional property feature values and the target functional property feature values, wherein the distance comprises a distance between the weighted prototype functional property feature values and the weighted target functional property feature values. 
     
     
         8 . The method of  claim 1 , further comprising measuring a binary characteristic of the prototype sample, wherein the set of manufacturing variable values is determined further based on the binary characteristic. 
     
     
         9 . The method of  claim 1 , wherein training the model comprises:
 measuring a training functional property signal for a training sample, wherein the training sample is associated with a set of training manufacturing variable values;   extracting training functional property feature values from the training functional property signal; and   training the model to predict the training functional property feature values based on the set of training manufacturing variable values.   
     
     
         10 . The method of  claim 1 , wherein the model comprises an encoder trained to encode functional property feature values and manufacturing variable values. 
     
     
         11 . The method of  claim 1 , wherein the target functional property signal comprises a measurement for at least one of: texture, melt, or flavor. 
     
     
         12 . The method of  claim 1 , wherein the target sample comprises a dairy product. 
     
     
         13 . A method, comprising:
 measuring a functional property signal for a sample, wherein the sample is manufactured according to a set of variable values;   extracting functional property feature values from the functional property signal;   determining a sample classification for the sample based on the functional property feature values, using a trained model; and   determining a set of updated variable values based on the sample classification.   
     
     
         14 . The method of  claim 13 , wherein the functional property signal comprises a data time series, wherein the functional property feature values are extracted using time series analysis. 
     
     
         15 . The method of  claim 13 , wherein the functional property features comprise non-semantic features. 
     
     
         16 . The method of  claim 13 , wherein the model is trained using training data comprising training functional property feature values labeled with associated sample classifications. 
     
     
         17 . The method of  claim 13 , wherein training the model comprises clustering training functional property feature values into a set of clusters, wherein determining the sample classification for the sample is determined by using the model to select a cluster from the set of clusters based on the functional property feature values for the sample. 
     
     
         18 . The method of  claim 13 , wherein the model is trained using adversarial machine learning methods. 
     
     
         19 . The method of  claim 13 , wherein the set of updated variable values are determined using explainability methods applied to the trained model.