Natural language understanding model with context resolver

A system comprising:

• a non-transitory computer readable storage media;
• and
• a processor configured to:
• receive a natural language input corresponding to at least one of a desired intent and a desired entity;
• determine, via the natural language processor, a domain of known intents and known entities based on the natural language input and a natural language understanding (NLU) model;
• score known intents corresponding to the domain based on the natural language input and the NLU model to generate an intent confidence score for each known intent;
• score known entities corresponding to the domain based on the natural language input to generate an entity confidence score for each known entity;
• compare the intent confidence scores and entity confidence scores to a threshold value;
• determine that the natural language input does not correspond to at least one of the known intents and the known entities based on the comparing, wherein the natural language input substantially matches terms corresponding to at least one of the known intents and the known entities;
• retrain the NLU model, via the natural language processor, by mining historical data for historical use of the natural language input to generate an updated NLU model, and comprising:
• scoring all known entities corresponding to all known intents of all known domains based on the natural language input ranking all known entities based on the scoring to generate a highest ranked known entity;
• updating confidence scores of known intents based on the highest ranked known entity;
• comparing the updated confidence scores of the known intents to a second threshold value;
• and
• in response to at least one of the updated confidence scores of known intents meeting the second threshold value:
• ranking the known intents based on the updated confidence scores to generate a highest ranked known intent;
• and
• determining at least one of:
• a new intent corresponds to the highest ranked known intent, and
• a new entity corresponds to the highest ranked known entity;
• and
• determine the natural language input corresponds to at least one of the new intent and the new entity based on the updated NLU model.

One or more non-transitory computer readable storage media encoded with instructions that, when executed by a processor, cause the processor to:

• receive a natural language input corresponding to at least one of a desired intent and a desired entity;
• determine, via the natural language processor, a domain of known intents and known entities based on the natural language input and a natural language understanding (NLU) model;
• score known intents corresponding to the domain based on the natural language input and the NLU model to generate an intent confidence score for each known intent;
• score known entities corresponding to the domain based on the natural language input to generate an entity confidence score for each known entity;
• compare the intent confidence scores and entity confidence scores to a threshold value;
• determine that the natural language input does not correspond to at least one of the known intents and the known entities based on the comparing, wherein the natural language input substantially matches terms corresponding to at least one of the known intents and the known entities;
• retrain the NLU model, via the natural language processor, by mining historical data for historical use of the natural language input to generate an updated NLU model, the retraining comprising:
• scoring all known entities corresponding to all known intents of all known domains based on the natural language input;
• ranking all known entities based on the scoring to generate a highest ranked known entity;
• updating confidence scores of known intents based on the highest ranked known entity;
• comparing the updated confidence scores of the known intents to a second threshold value;
• and
• in response to at least one of the updated confidence scores of known intents meeting the second threshold value:
• ranking the known intents based on the updated confidence scores to generate a highest ranked known intent;
• and
• determining at least one of:
• a new intent corresponds to the highest ranked known intent, and
• a new entity corresponds to the highest ranked known entity;
• and
• determine the natural language input corresponds to at least one of the new intent and the new entity based on the updated NLU model.