METHOD TO ANALYZE PERISHABLE FOOD STOCK PREDICTION

1 . A computer-implemented method of predicting perishable food stock quantity for replenishment, the method executed by at least one hardware processor communicatively coupled to a network of computers, comprising:

receiving a user input comprising at least a product identifier of a product about which a quantity to replenish is to be predicted;

creating a search strategy comprising searching at least unstructured multiple dimensions of data stored on the network of computers based on the user input;

performing a search of the network of computers according to the search strategy;

invoking a machine learning model associated with a dimension, for each of the multiple dimensions, with a result of the search generated into a feature vector as input to the machine learning model, the machine learning model outputting a replenishment quantity along each of the multiple dimensions, the output of the machine learning model representing candidate answers;

selecting supporting evidence associated with the candidate answers;

merging the candidate answers of the multiple dimensions; and

providing a result of the merged output quantities as a predicted suggestion.

2 . The method of claim 1 , wherein the multiple dimensions comprises product information, retail features, target consumer class, geographic location, economic news and weather forecasts.

3 . The method of claim 1 , further comprising scoring the candidate answers.

4 . The method of claim 1 , further comprising training the machine learning model to predict the replenishment quantity.

5 . The method of claim 2 , wherein the machine learning model is trained to predict the replenishment quantity separately along each of the multiple dimensions.

6 . The method of claim 1 , further comprising receiving user feedback associated with the predicted suggestion and retraining the machine learning model based on the user feedback.

7 . A computer readable storage medium storing a program of instructions executable by a machine to perform a method of predicting perishable food stock quantity for replenishment, the method comprising:

receiving a user input comprising at least a product identifier of a product about which a quantity to replenish is to be predicted;

creating a search strategy comprising searching at least unstructured multiple dimensions of data stored on the network of computers based on the user input;

performing a search of the network of computers according to the search strategy;

invoking a machine learning model associated with a dimension, for each of the multiple dimensions, with a result of the search generated into a feature vector as input to the machine learning model, the machine learning model outputting a replenishment quantity along each of the multiple dimensions, the output of the machine learning model representing candidate answers;

selecting supporting evidence associated with the candidate answers;

merging the candidate answers of the multiple dimensions; and

providing a result of the merged output quantities as a predicted suggestion.

8 . The computer readable storage medium of claim 7 , wherein the multiple dimensions comprises product information, retail features, target consumer class, geographic location, economic news and weather forecasts.

9 . The computer readable storage medium of claim 7 , further comprising scoring the candidate answers.

10 . The computer readable storage medium of claim 7 , further comprising training the machine learning model to predict the replenishment quantity.

11 . The computer readable storage medium of claim 8 , wherein the machine learning model is trained to predict the replenishment quantity separately along each of the multiple dimensions.

12 . The computer readable storage medium of claim 7 , further comprising receiving user feedback associated with the predicted suggestion and retraining the machine learning model based on the user feedback.

13 . A system of predicting perishable food stock quantity for replenishment, comprising:

at least one hardware processor communicatively coupled to a network of computers, the at least one hardware processor operable to perform at least:

receiving a user input comprising at least a product identifier of a product about which a quantity to replenish is to be predicted;

creating a search strategy comprising searching at least unstructured multiple dimensions of data stored on the network of computers based on the user input;

performing a search of the network of computers according to the search strategy;

invoking a machine learning model associated with a dimension, for each of the multiple dimensions, with a result of the search generated into a feature vector as input to the machine learning model, the machine learning model outputting a replenishment quantity along each of the multiple dimensions, the output of the machine learning model representing candidate answers;

selecting supporting evidence associated with the candidate answers;

merging the candidate answers of the multiple dimensions; and

providing a result of the merged output quantities as a predicted suggestion.

14 . The system of claim 13 , wherein the multiple dimensions comprises product information, retail features, target consumer class, geographic location, economic news and weather forecasts.

15 . The system of claim 13 , wherein the at least one hardware processor is further operable to score the candidate answers.

16 . The system of claim 13 , wherein the at least one hardware processor is further operable to train the machine learning model to predict the replenishment quantity.

17 . The system of claim 14 , wherein the at least one hardware processor is further operable to train the machine learning model to predict the replenishment quantity separately along each of the multiple dimensions.

18 . The system of claim 13 , wherein the at least one hardware processor is further operable to receive user feedback associated with the predicted suggestion and retrain the machine learning model based on the user feedback.