Accenture Global Solutions Limited

Accenture Global Services Gmbh

Lan Guan

Anwitha Paruchuri

Christopher Yen-Chu Chan

Guanglei Xiong

Sukryool Kang

Jayashree Subrahmonia

John D Bolze

Neeraj D Vadhan

Sujeong Cha

Anupam Anurag Tripathi

Jill Gengelbach-wylie

Thomas Wayne Hancock

Louise Noreen Barrere

Yuan He

Thomas Kim

Wenxian Zhang

Ivan A Wong

Sundaravadivelan Ramamoorthy

Aiperi Iusupova

Surya Raghavendra Vadlamani

Danielle Moffat

William L Gewirtz

Andrew Francis Hickl

Athina Kanioura

Jing Su Brewer

Ivan Wong

Aaron James Sander

Saurabh Mathur

Nivedita Samal

Soumya Singh

Madhusudhan Srivatsa Chakravarthi

Lana Grimes

Arun Venkatesan

Mannbir Singh

Ajit Ferrao

Nilesh Shinde

Stephen M Dadoly

John A Morgan

Paul J Bultema

Dhruv Garg

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

The present disclosure relates to systems, methods, and products for using machine-learning networks to generate trustworthy audio and face mesh. A system, serving as a digital avatar, generates a trust audio and trust face mesh corresponding to an input text. A method includes generating a set of trust embedding vectors based on a reference audio; generate a text embedding vector based on the input text; generate a conditioned vector based on the set of trust embedding vectors and the text embedding vector; synthesize an audio representation based on the conditioned vector; generate the trust audio based on the synthesized audio representation; obtain a speech feature representation based on the trust audio; obtain an abstract feature vector based on the speech feature representation; and generate positions of vertices based on the abstract feature vector, the positions of vertices being used for generating the trust face mesh.

Systems and methods to automate trust delivery

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

A digital content communication system for account management and predictive analytics may be provided. The system may include an analytics system that communicates with one or more servers and one or more data stores to provide digital content management in a network. The analytics system may include a data access interface to receive data associated with a customer, as well as a processor to: standardize the received data using a standardization technique; process the standardized data using a dark data processing technique; generate a customer fit score and a digital density score based on the dark data processing of the standardized data; match received data associated with a customer against at least one variable using at least one matching technique; create a lead analytical record (LAR); prioritize leads in the LAR using a predictive modeling technique; and establish optimized channel assignment based on at least one of the customer fit score, the digital intensity score, the LAR, or the matching and prioritization actions.

Digital content prioritization to accelerate hyper-targeting

Systems and methods for content management are disclosed. A content management system may include a data sourcing and data streaming engine configured to aggregate content data from data sources, a trend detection and monitoring engine configured to match data sources with content management metadata and to provide relevance scoring of the content data, and a trend recommendation and visualization engine configured to present to a user (e.g., content reviewer or subject matter expert), through a graphical user interface, an output comprising a relevance score and relevant trend, topic, and/or data source information, and to receive from the user through the graphical user interface input and/or activity. The data sourcing and data streaming engine, the trend detection and monitoring engine, and/or the trend recommendation and visualization engine may be updated with the input and/or activity for processing subsequent content data.

Systems and methods for content management

A system implements a method of migrating users to target services including segmenting the users of the target services into macrosegments and micro segments within each macro segment. For each micro segment, rankings of the target services for the users in the micro segment are determined. The rankings are based on the combinations of the target services owned by the users in the micro segment product association, a sequence for acquiring the target services owned by the users in the micro segment, propensities to acquire the target services, and eligibility of the target services for the users in the micro segment. The target services are selected for migration based on the rankings.

Migration system to migrate users to target services

A customized content selection and delivery system is operable to create a customized content channel package for a subscriber and customized content channel package to a customer premises over a content delivery network. A multi-level analysis based on measured content consumption metrics is performed to select content provider channels for the customized content channel package.

Customized content channel generation and delivery for service providers

A service provider network migration system migrate services from a first network infrastructure to a second network infrastructure. A joined circuits dataset is created that associates circuits in the first network infrastructure with a service provided by the circuit and a customer receiving the service via the first network infrastructure. A product mappings dataset is created that maps each service in the first network infrastructure to at least one target service to be provided in the second network infrastructure. A migration sequencing server creates a migration plan according to the information in the datasets, and forecasts for customer migration rate and revenue generation for the target services are estimated. A provisioning server configures parameters for the circuits in the second network infrastructure via a network according to the migration plan to deploy the target services in the second network infrastructure.

Service provider network migration

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Johns Creek, GA, United States of America

Lan Guan