Abstract: A device trains a classification model with defect classifier training data to generate a trained classification model and processes information indicating priorities and rework efforts for defects, with a Pareto analysis model, to select a set of classes for the defects. The device calculates defect scores for the set of the classes and selects a particular class, from the set of the classes, based on the defect scores. The device processes a historical data set for the particular class to identify a root cause corrective action (RCCA) recommendation and processes information indicating a defect associated with the particular class, with the trained classification model, to generate a predicted RCCA recommendation for the defect. The device processes the predicted RCCA recommendation and the RCCA recommendation, with a linear regression model, to determine an effectiveness score for the predicted RCCA recommendation and retrains the classification model based on the effectiveness score.

Abstract: A system and method for using, training, building, and managing a question and answer engine to automatically generate responses to an end-user is disclosed. Specifically, the method and system make use of a topic builder that uses cluster predictions to generate and identify a list of topics and subtopics. A question and answer database may then be sorted by topic and subtopic using a similarity scorer. New user utterances may be analyzed to identify questions, with a cluster predictor identifying the corresponding topic and subtopics for each question, and a similarity scorer may identify the closest known question for the user's question to a recommender as an answer. Analytics of new user utterances are tracked to update the historical utterance database and question and answer database, thus allowing continuous improvement of the engine.

Abstract: The present disclosure relates to a system, a method, and a product for an artificial intelligence based virtual agent trainer. The system includes a processor in communication with a memory storing instructions. When the processor executes the instructions, the instructions are configured to cause the processor to obtain input data and generate a preliminary set of utterances based on the input data, process the preliminary set of utterances to generate a set of utterance training data, generate a set of conversations based on the set of utterance training data, simulate the set of conversations on a virtual agent to obtain a conversation result, verify an intent and a response based on the conversation result, verify a use case flow and flow hops based on the conversation result, and generate recommendation information and maturity report based on verification results.

Abstract: The present disclosure relates to a system, a method, and a product for an artificial intelligence based virtual agent trainer. The system includes a processor in communication with a memory storing instructions. When the processor executes the instructions, the instructions are configured to cause the processor to obtain input data and generate a preliminary set of utterances based on the input data, process the preliminary set of utterances to generate a set of utterance training data, generate a set of conversations based on the set of utterance training data, simulate the set of conversations on a virtual agent to obtain a conversation result, verify an intent and a response based on the conversation result, verify a use case flow and flow hops based on the conversation result, and generate recommendation information and maturity report based on verification results.

Abstract: A system and method for using, training, building, and managing a question and answer engine to automatically generate responses to an end-user is disclosed. Specifically, the method and system make use of a topic builder that uses cluster predictions to generate and identify a list of topics and subtopics. A question and answer database may then be sorted by topic and subtopic using a similarity scorer. New user utterances may be analyzed to identify questions, with a cluster predictor identifying the corresponding topic and subtopics for each question, and a similarity scorer may identify the closest known question for the user's question to a recommender as an answer. Analytics of new user utterances are tracked to update the historical utterance database and question and answer database, thus allowing continuous improvement of the engine.

Abstract: A device trains a classification model with defect classifier training data to generate a trained classification model and processes information indicating priorities and rework efforts for defects, with a Pareto analysis model, to select a set of classes for the defects. The device calculates defect scores for the set of the classes and selects a particular class, from the set of the classes, based on the defect scores. The device processes a historical data set for the particular class to identify a root cause corrective action (RCCA) recommendation and processes information indicating a defect associated with the particular class, with the trained classification model, to generate a predicted RCCA recommendation for the defect. The device processes the predicted RCCA recommendation and the RCCA recommendation, with a linear regression model, to determine an effectiveness score for the predicted RCCA recommendation and retrains the classification model based on the effectiveness score.

Abstract: The present disclosure relates to a system, a method, and a product for an artificial intelligence based virtual agent trainer. The system includes a processor in communication with a memory storing instructions. When the processor executes the instructions, the instructions are configured to cause the processor to obtain input data and generate a preliminary set of utterances based on the input data, process the preliminary set of utterances to generate a set of utterance training data, generate a set of conversations based on the set of utterance training data, simulate the set of conversations on a virtual agent to obtain a conversation result, verify an intent and a response based on the conversation result, verify a use case flow and flow hops based on the conversation result, and generate recommendation information and maturity report based on verification results.