Patent ID: 11893354
Assignee: COGNIZANT TECHNOLOGY SOLUTIONS INDIA PVT. LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. A system for improving a training dataset, said training dataset comprising one or more utterances-intent pairs, the system comprising:
a memory storing program instructions; at least one processor configured to execute program instructions stored in the memory; and an error detection engine executed by the at least one processor, and configured to:
train a plurality of machine learning models with the training dataset to obtain a diverse set of trained Machine Learning (ML) models;
feed each utterance of the one or more utterances-intent pairs as an input to the diverse set of trained ML models to obtain respective intent predictions for each utterance;
evaluate a probability of error associated with each utterances-intent pair of the training dataset based on an analysis of the respective intent predictions for each utterance, wherein a mismatch during mapping the intent prediction for each utterance from each of the diverse set of ML models with the intent associated with the utterance in the training dataset and a similarity score (S) associated with the intent predictions for each utterance less than or equal to a predefined similarity-threshold (ST) is indicative of a high probability of error, the similarity score (S) is representative of percentage of ML models out of the diverse set of ML models providing similar intent predictions for same utterance, a mismatch during the mapping and the similarity score (S) greater than or equal to the predefined similarity-threshold (ST) is indicative of a high probability of error, a match during the mapping and the similarity score (S) less than or equal to the predefined similarity-threshold (ST) is indicative of a high probability of error, and a match during the mapping and the similarity score (S) greater than or equal to the predefined similarity-threshold (ST) is indicative of a low probability of error; and
generate a set of improvement recommendations associated with each utterances-intent pair of the training dataset based on the evaluated probability of error.