Accenture Global Solutions Limited

Gaurav Mengi

Vidya Rajagopal

Marin Grace Mercylawrence

Kokila Manickam

Bijayani Das

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.

Utilizing artificial intelligence to generate and update a root cause analysis classification model

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.

System and method for using a question and answer engine

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.

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Bengaluru, India

Gaurav Mengi