Accenture Global Solutions Limited

Jayashree Subrahmonia

Guanglei Xiong

Lan Guan

Anwitha Paruchuri

Sukryool Kang

Christopher Yen-Chu Chan

Sujeong Cha

Anupam Anurag Tripathi

Neeraj D Vadhan

Louise Noreen Barrere

Thomas Wayne Hancock

Jill Gengelbach-wylie

Wenxian Zhang

Yuan He

Danielle Moffat

Ivan A Wong

Aaron James Sander

Jing Su Brewer

The present disclosure relates to a system, a method, and a product for using machine learning models to quantify and/or improve trust in conversations. The system includes a non-transitory memory; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to: obtain a set of vocal features and a set of text features for each sample in audio samples; obtain a trust score for each sample; perform a preprocess to obtain a set of input features for each sample; determine a type of machine-learning algorithm for the machine-learning network; tune a set of hyper parameters for the machine-learning network; generate a predicated trust score by the machine-learning network with the sets of input features for each sample; and train the machine-learning network based on the predicated trust score and the trust score for each sample to obtain the training result.

Systems and methods to improve trust in conversations

Implementations are directed to receiving a set of tuples, each tuple including an entity and a product from a set of products, for each tuple: generating, by an embedding module, a total latent vector as input to a recommender network, the total latent vector generated based on a structural vector, a textual vector, and a categorical vector, each generated based on a product profile of a respective product and an entity profile of the entity, generating, by a context integration module, a latent context vector based on a context vector representative of a context of the entity, and inputting the total latent vector and the latent context vector to the recommender network, the recommender network being trained by few-shot learning using a multi-task loss function, and generating, by the recommender network, a prediction including a set of recommendations specific to the entity.

Few-shot learning for multi-task recommendation systems

The present disclosure relates to a system, a method, and a product for using deep learning models to quantify and/or improve trust in conversations. The system includes a non-transitory memory storing instructions executable to construct a deep-learning network to quantify trust scores; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to: obtain a trust score for each voice sample in a plurality of audio samples, generate a predicated trust score by the deep-learning network based on each voice sample in the plurality of audio samples, wherein the deep-learning network comprises a plurality of branches and an aggregation network configured to aggregate results from the plurality of branches, and train the deep-learning network based on the predicated trust score and the trust score for each voice sample to obtain a training result.

Systems and methods to improve trust in conversations with deep learning models

A system and method for lead conversion using conversational virtual avatar is disclosed. System comprising processor causes Conversation Virtual Avatar Platform (CVAP) to receive, for first entity, from lead prioritization engine, leads applicable to first entity via lead repository based on scores associated with respective leads. Processor causes CVAP to receive, through conversation management engine (CME) configured in CVAP, from leads, responses to questions pertaining to product attributes and information pertaining to lead. The processor causes CVAP to process responses to determine action and/or state, which includes whether to issue additional product-attribute based questions through Virtual Avatar (VA) using Response To Motion Module (RTME) or to, through recommender engine that uses recommendation model, in real-time, recommend products associated with first entity to respective at least one lead based on any or combination of responses received from lead, information pertaining to lead, and products ordered by entities similar to lead.

System, method, and medium for lead conversion using a conversational virtual avatar

Implementations of the present disclosure include receiving a goal, providing a problem-specific knowledge graph that is responsive to at least a portion of the goal, determining a set of events from the problem-specific knowledge graph, processing data representative of events in the set of events through a first machine learning (ML) model to provide a set of event scores, each event score in the set of event scores being associated with a respective event in the set of events, determining a sub-set of events based on the set of event scores, for each event in the sub-set of events, determining at least one action by processing a sequence of actions through a second ML model, and outputting the sub-set of events and a set of actions for execution of at least one action in the set of actions.

Early pattern detection in data for improved enterprise operations

Growing community of inventors

San Jose, CA, United States of America

Jayashree Subrahmonia