Patent ID: 11966697
Assignee: BANK OF AMERICA CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method for training a machine-learning (“ML”)-based language-processing system with automated tuning, the method comprising:
compiling a training set, said training set that is stored in a database and comprises a plurality of utterances mapped to a plurality of intents;
assigning a set of features to each of the plurality of utterances;
training an ML model, using the training set, to derive a comprehension of an input utterance;
identifying a set of inoperative input utterances and a set of operative input utterances, wherein an inoperative utterance is an utterance that returns a null intent response;
deriving a first accuracy score for the set of inoperative input utterances and for the set of operative input utterances;
iterating through the set of inoperative input utterances and the set of operative input utterances based on the derived accuracy score, such that when a highest scoring intent for an utterance is flagged as inaccurate, the method further comprises attempting to determine whether the second highest scoring intent is inaccurate; and
tuning the ML model, said tuning comprising:
retrieving a set of features associated with each inoperative input utterance from the set of inoperative input utterances;
generating an updated utterance-feature-intent (“UFI”) mapping;
retraining the ML model, in a controlled environment, using the updated UFI mapping;
generating an updated accuracy score for the set of inoperative input utterances and for the set of operative input utterances, said updated accuracy score based on the retrained ML model;
when the updated accuracy score for the set of inoperative input utterances is less than or equal to the first accuracy score for the set of inoperative input utterances, and the updated accuracy score for the set of operative input utterances is greater than or equal to the first accuracy score for the set of operative input utterances, amplifying the impact of the updated UFI mapping by retraining the ML model with a plurality of instances of the updated UFI mapping; and
when the updated accuracy score for the set of inoperative input utterances is greater than the first accuracy score for the set of inoperative input utterances, and the updated accuracy score for the set of operative input utterances is greater than or equal to the first accuracy score for the set of operative input utterances, deploying the updated UFI mapping wherein the amplifying the impact of the updated UFI mapping is executed incrementally, said incremental execution comprising:
retraining the ML model with a first instance of the updated UFI mapping;
generating a new updated accuracy score for the set of inoperative input utterances and for the set of operative input utterances;
when the new updated accuracy score for the set of inoperative input utterances is still less than or equal to the first accuracy score for the set of inoperative input utterances, and the new updated accuracy score for the set of operative input utterances is still greater than or equal to the first accuracy score for the set of operative input utterances, retraining the ML model with another instance of the updated UFI mapping; and

wherein the ML model is retrained with a maximum of three instances of the updated UFI mapping.