Accenture Global Solutions Limited

Ajay Chatterjee

Shubhashis Sengupta

Anutosh Maitra

Milind Ravikiran Savagaonkar

Abhisek Mukhopadhyay

Shivam Garg

An intelligent question and answer (Q&A) system and method for interactively guiding users through a procedure is disclosed. The intelligent Q&A system can dynamically generate process trees (or procedural trees) from the content or procedures presented in a raw document, such as a reference manual. The intelligent Q&A system can include a virtual agent that uses the dynamically generated process trees for interactive conversation with a user. Using the system, the virtual agent can interactively guide users through completing tasks such as updating software or connecting an IoT device to an existing system.

System and method for interactively guiding users through a procedure

A system and method for training a virtual agent to identify a user's intent from a conversation is disclosed. The system and method use an iterative process of clustering multiple conversations (converted into feature representations) used for training a machine learning model into labeled clusters having similar user intents. Clustering enables labeling a large number of training conversations efficiently. The labeled clusters may be used to train a virtual agent to classify the conversational intent of a conversation. Then, the machine learning model can classify future conversations based on similarity to labeled clusters. By knowing a human user's intent, a virtual agent can deliver what the user desires.

System and method for training a virtual agent to identify a user's intent from a conversation

A device receives a corpus of text documents, and utilizes feature extraction on a text document, of the corpus of text documents, to generate features from the text document, where the features include binary features, numeric features, and categorical features. The device performs feature engineering on one or more of the binary features, the numeric features, or the categorical features, to generate converted features, and performs feature encoding on the text document, based on the converted features, to represent the text document as a vector with a similarity score for a domain. The device provides the vector with the similarity score for the domain, as training data, to a machine learning model to generate a trained machine learning model, and performs an action using the trained machine learning model.

Automated and optimal encoding of text data features for machine learning models

A system and method for generating a conversation graph for a group of related conversations is disclosed. The system and method use an iterative process of clustering multiple conversations into labeled clusters having similar user intents. The labeled clusters may be used to train a virtual agent to classify the conversational intent of a conversation. Utterances by the agent and/or customer in each conversation from a group of conversations about a similar task or goal can be processed and the dialogue categorized. The resultant classifications are used to represent the many conversations in a single graph by a plurality of nodes interconnected by transitional paths that indicate the conversation flow.

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Bengaluru, India

Ajay Chatterjee