Accenture Global Services Gmbh

Accenture Global Solutions Limited

Alex M Kass

Kunal Verma

Lucian P Hughes

Reymonrod Geli Vasquez

Manish Mehta

Vibhu Saujanya Sharma

Santonu Sarkar

Edy S Liongosari

Gurdeep Singh

Sunil Shettigar

Niclas Almquist

Alpana A Dubey

Owen Edward Richter

Mary Elizabeth Hamilton

Venkatesh K Subramanian

Robert Dooley

Srinivas Yelisetty

Peter Z Yeh

Dana Le

Taurean Dyer

Michael J Biltz

Gurdeep Virdi

Nikolas Martelaro

Neil Sheth

Santhosh Kumar Shivaram

Upendra Chintala

Christopher Cowell-Shah

Daniel R Farina

Matthew Thomas Short

Dadong Wan

Kumar Abhinav

Anurag Dwarakanath

Sakshi Jain

Suma Mani Kuriakose

Senthil Ramani

Grace T Cheng

David William Vinson

Peter Zei-Chan Yeh

Gary Titus

Veenu Arora

Deepmala Gupta

Jordan K Buller

Shaw-Yi Chaw

Marc Carrel-Billiard

David H Nguyen

Taemie J Kim

Alexandria Emily Pabst

Timothy Li

Arun Panayappan

Derrick Anthony Martin, Ii

Sakshi Taneja

Brett Goldstein

Masoud Loghmani

Omar Tahboub

David Q Sun

David Morse

Maria P Ergueta

Malharrao Mujumdar

Venkatraman Vaidhyanathan

Reshma Nuggehally

Sumit Dayal

Mary Catherine Thomas

Kahlil Gibran Fitzgerald

Rajiv Argawal

Santonu Sakar

Charles Jacob Foster

Owen E Richter

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing an olfaction training program by an online service executing on a server. A first object and an aroma of the first object is transmitted to the client device of a user that presents the first object in a virtual reality environment and disperses the first aroma. In response, the user interacts with the vital reality environment. The user response is transmitted back to the online service. The user response is stored in a database along with the first data and the first aroma. The online service determines a second object and an aroma of the second object based on the user response. The second object and the aroma of the second object is transmitted to the client device.

Aroma training using virtual reality

This specification relates to methods, and systems for stakeholder and impact discovery. One of the methods includes receiving a knowledge representation model indicating at least a first stakeholder and a first impact; determining an impact model based at least in part on the knowledge representation model; receiving distributed sensor data; and determining at least a second impact based at least in part on the impact model and the distributed sensor data.

Stakeholder and impact discovery

Implementations for receiving, by the design assistant, an initial set of requirements, each requirement in the initial set of requirements being manually provided by a user, providing, by the design assistant and using a knowledge graph, a first set of questions based on the initial set of requirements and content determined from at least one design document in a set of design documents, filtering, by the design assistant, one or more questions from the first set of questions to provide a second set of questions, providing, by the design assistant, at least a portion of the second set of questions as output to the user to elicit one or more responses from the user, and editing, by the design assistant, the initial set of requirements based on the one or more responses to provide a set of requirements, the set of requirements for the to-be-designed product.

Intelligent design platform using digital assistants for design process support

The global proliferation of high speed communication networks has created unprecedented opportunities for geographically distributed resource interaction. A crowd sourced project management synthesizer provides crowd sourced project management features, as well as resource performance tracking features, to provide efficiencies to the project's implementation.

Crowd sourced resources as selectable working units

This document describes multi-modal platforms that receive multi-modal inputs and generates interactive experiences based on the inputs. In one aspect, a method includes receiving input data comprising one or more images of an area of a sports venue. A person is detected in the one or more images. Apparel information including at least one of a team identifier or player identifier on athletic apparel of the person is identifier in the one or more images. An interactive experience that includes a particular sports-related character is selected based at least on the apparel information. The interactive experience is initiated on an interactive display. The interactive experience includes video of the particular sports-related character delivering a message to the person.

Platform for generating interactive experiences at sports venues

This document describes impact networks that enable stakeholders to measure and take actions to adjust the impact of changes to products and services. In one aspect, a method includes receiving a stakeholder impact graph defining stakeholders. Each stakeholder is a node that is connected to at least one other stakeholder by an impact relationship edge. Data defining a first design perturbation in one or more design properties for a product or service of a first stakeholder is received from the first stakeholder. A determination is made, for one or more second stakeholders of the stakeholders connected to the first stakeholder by a respective impact relationship edge, a respective impact on the second stakeholder caused by the first design perturbation. Data defining a remediation perturbation that adjusts the respective impact of the first deign perturbation on the one or more second stakeholders is received.

Impact network

Techniques are described for component design based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing use of one or more components of the system by one or more individuals. The sensor data from various instances of the component may be aggregated and analyzed to determine update(s) to the design of the component. For example, the aggregate sensor data may be analyzed to identify portions of the component frequently associated with movements by users (e.g., fidgeting, adjustments). The identified portion(s) can be presented graphically in a design view used to specify design modification(s) for the component. In some implementations, the aggregate sensor data is provided as input to a model that is trained, using machine learning, to output design modification(s) for the component based on the input aggregate sensor data.

Component design based on sensor data

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a predictive analytics system that provides a mechanism to change the design or implementations of a product manufactured in a supply chain are disclosed. In one aspect, a method includes the actions of receiving training data that includes private information for a node in a supply chain network and information regarding previous decisions related to product change requests for a product manufactured through the supply chain network; training, using the training data, a predictive model configured to render decisions for requests to change a part used in manufacturing the product; receiving a request to change a given part; applying the predictive model to the request to change the given part; determining a decision approving or denying the request; and transmitting the decision to the requesting node.

Adaptive predictive analytics for design modification requests

The global proliferation of high speed communication networks has created unprecedented opportunities for geographically distributed resource interaction. However, while the opportunities exist and continue to grow, the realization of those opportunities has fallen behind. A resource virtualization system solves the enormous technical challenges of finding and evaluating resources to assign to complex projects.

Resource virtualization

An asset tracking system helps an enterprise correctly and efficiently determine when assets are created and when they are re-used. Re-use of assets often creates a positive impact, e.g., on other projects ongoing within the enterprise. The system detects the asset re-use, determines the asset impact, and automatically updates a dynamic profile of the asset creator to register the value of their work. In some implementations, the system implements a virtual currency system that also rewards the supervisors of the asset creator, and allows the virtual currency to be exchanged for project resources needed by the supervisors.

Asset registration and detection system with closed-loop feedback channel

The global proliferation of high speed communication networks has created unprecedented opportunities for geographically distributed resource interaction. However, while the opportunities exist and continue to grow, the realization of those opportunities has fallen behind. A dynamic process execution architecture solves the enormous technical challenges of providing effective and efficient process execution environments for geographically distributed resources to execute a complex project.

Dynamic process execution architecture with disassociated resource control

A device may include one or more processors. The device may receive first data to be processed. The device may receive information identifying a plurality of tasks to be performed to process the first data. The device may determine that one or more first tasks, of the plurality of tasks, are to be performed by an automation device, and that one or more second tasks, of the plurality of tasks, are to be performed by workers. The device may provide, to the automation device and to the workers, the first data. The device may receive, from the automation device and the workers, second data. The second data may be generated based on the automation device performing the one or more first tasks and based on the workers performing the one or more second tasks.

Data processing techniques

Techniques are described for component configuration based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing the use of component(s) of the system by individual(s). The sensor data is analyzed (e.g., in real time) to determine an updated configuration for component(s) (e.g., an adjustment to the seat back, lumbar support of a car seat, etc.). The updated configuration may be communicated to the individual as a recommended configuration. In some implementations, the updated configuration may be communicated directly to the component which, on receiving and processing the configuration update, sends signals to various actuators to move the subcomponents of the component into the updated configuration. In some implementations, the sensor data is used to train, through machine learning, a model that provides configuration update(s) for component(s) based on the input sensor data.

Component configuration based on sensor data

A system may receive task information identifying tasks to be performed by workers of one or more crowds. The system may obtain worker information describing the workers. The system may determine task completion probabilities based on the task information and the worker information. A task completion probability may identify a likelihood that a particular crowd will complete a particular task. The system may determine crowd recommendation information based on the task completion probabilities, the task information, and/or the worker information. The crowd recommendation information may identify recommended crowds to which a task is recommended to be provided. The system may output the crowd recommendation information and/or the worker information to permit selection of the crowds to perform the task.

Crowdsourcing a task

Growing community of inventors

Palo Alto, CA, United States of America

Alex M Kass