Correlating request and response data using supervised learning

A non-transitory computer readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining steps of:

• retrieving a request and a plurality of responses;
• filtering the plurality of responses by removing responses having a field value that conflicts with a corresponding field value of the request;
• generating packed candidates based on the request and the plurality of responses remaining after the filtering by iteratively computing permutations of responses from the plurality of responses until reaching a threshold number of permutations, a given packed candidate in the packed candidates comprising the request and a subset of the plurality of responses, wherein an aggregate value computed from a numerical field of the responses in the subset satisfies a condition of the request;
• generating feature vectors corresponding to the packed candidates, a given feature vector in the feature vectors including at least one synthesized feature computed from fields of both the request and one or more corresponding responses in a respective packed candidate, the at least one synthesized feature comprising a computed temporal difference between a date associated with the request and dates associated with the one or more corresponding responses;
• inputting the feature vectors into a machine learning (ML) model trained on previously labeled packed candidates, the ML model configured to output, for each of the feature vectors, a score representing a likelihood that the respective packed candidate accurately bundles the one or more corresponding responses with the request;
• selecting a feature vector from the feature vectors having a highest score from the scores output by the ML model;
• and
• storing responses associated with the feature vector and the request in a data storage device.

A device comprising:

• a processor configured to:
• retrieve a request and a plurality of responses;
• filter the plurality of responses by removing responses having a field value that conflicts with a corresponding field value of the request;
• generate packed candidates based on the request and the plurality of responses remaining after the filtering by iteratively computing permutations of responses from the plurality of responses until reaching a threshold number of permutations, a given packed candidate in the packed candidates comprising the request and a subset of the plurality of responses, wherein an aggregate value computed from a numerical field of the responses in the subset satisfies a condition of the request;
• generate feature vectors corresponding to the packed candidates, a given feature vector in the feature vectors including at least one synthesized feature computed from fields of both the request and one or more corresponding responses in a respective packed candidate, the at least one synthesized feature comprising a computed temporal difference between a date associated with the request and dates associated with the one or more corresponding responses;
• input the feature vectors into a machine learning (ML) model trained on previously labeled packed candidates, the ML model configured to output, for each of the feature vectors, a score representing a likelihood that the respective packed candidate accurately bundles the one or more corresponding responses with the request;
• select a feature vector from the feature vectors having a highest score from the scores output by the ML model;
• and
• store responses associated with the feature vector and the request in a data storage device.