Accenture Global Solutions Limited

Accenture Global Services Gmbh

Anthony McCoy

Qurrat Ul Ain

Andrew Poole

Gaurav Tandon

Sanjeev Shukla

Rexall E Thexton

Colleen Connolly

Sidath Mudiyanselage

Hannah Craddock

Farbod Kamiab

Carl Sharpe

Aoife Whelan

Md Faisal Zaman

Urvesh Bhowan

Bogdan Eugen Sacaleanu

Laura O'Malley

Navdeep Sharma

Sofian Hamiti

Jitesh Goyal

Antonio Penta

Ajitesh Prakash

Gavin Kearney

Phillip Lynch

A device may receive tracking data identifying trajectories of objects, and may annotate the tracking data to identify object categories for the tracking data. The device may identify objects based on the object categories, and may determine object trajectories for the objects. The device may transform the object trajectories to overhead planar trajectories, and may generate simplified object trajectories. The device may convert a continuous space associated with the simplified object trajectories into a discrete space, and may convert the discrete space to a lower dimensional space. The device may transform the object trajectories to binary feature vectors, and may process the binary feature vectors, with a clustering model, to determine trajectory clusters. The device may train a classification model with the binary feature vectors and the trajectory clusters, and may cause the trained classification model to be implemented.

Utilizing machine learning models to classify vehicle trajectories and collect road use data in real-time

A method for controlling access to one or more of a plurality of target systems includes receiving profile data that defines one or more features associated with a plurality of individuals with one or more entitlements of those individuals. Each entitlement is indicative of target system access. The method further includes generating a model that relates the one or more features and the one or more entitlements of the plurality of individuals. Profile data that defines one or more features associated with a target individual is received from a first user management system. A listing that includes one or more entitlements associated with the target individual, and confidence values associated with the one or more entitlements is generated based on the profile data and the model. Each confidence value is indicative of whether the target individual should be granted a corresponding entitlement. For each entitlement having a corresponding confidence value higher than a predetermined threshold, an instruction is communicated to a target system associated with the entitlement to allow the target individual access to the target system.

System for controlling access to target systems and applications

A data security and protection system that provides monitoring, diagnostics, and analytics within an enterprise network to identify potentially sensitive data is disclosed. The system may provide one or more data stores to store and manage personal data within a network. The system may also provide one or more servers to facilitate operations using information from the one or more data stores. The system may also provide an analytics system with processing components that determines uniqueness of personal data. The system may receive personal data and population attribute data via a data access interface. The analytics system may compare the data received to determine a fraction assignment, which when further processed using at least a combination or correlation technique, may yield a detailed uniqueness factor classification and analysis of the personal data to indicate its relative sensitivity. If there is risk associated with the sensitivity of the personal data, additional security actions may be taken by the data security and protection system.

Data security and protection system using uniqueness factor classification and analysis

System for controlling access to a plurality of target systems and applications

A method for controlling access to one or more of a plurality of target systems includes receiving profile data that defines one or more features associated with a plurality of individuals with one or more entitlements of those individuals. Each entitlement is indicative of target system access. The method further includes generating a model that includes one or more sets of rules where each set of rules is associated with an entitlement of the profile data. Each entitlement is indicative of target system/application access. Each rule within a set relates a combination of one or more features of the profile data with a confidence value. Profile data that defines one or more features associated with a target individual is received from a first user management system. A listing that includes one or more entitlements associated with the target individual, and confidence values associated with the one or more entitlements is generated based on the profile data and the rules. Each confidence value is indicative of whether the target individual should be granted a corresponding entitlement. For each entitlement having a corresponding confidence value higher than a predetermined threshold, an instruction is communicated to a target system associated with the entitlement to allow the target individual access to the target system.

A device may obtain information included in a corpus of documents relating to an organization. The device may identify a set of values indicating personal information for one or more individuals by using a set of natural language processing (NLP) techniques to analyze the information included in the corpus. The device may determine a set of relationships between one or more values, of the set of values indicating the personal information using one or more additional NLP techniques and/or one or more rules. The device may generate a set of user profiles for the one or more individuals based on the set of relationships between the one or more values indicating the personal information. The device may perform one or more actions associated with using the set of user profiles to service a request for information.

Automated secure identification of personal information

A device may receive information associated with an entity-specific designation of a role, and determine a standardized designation of the role that corresponds to the entity-specific designation of the role after comparing the information associated with the entity-specific designation of the role and information associated with a set of standardized designations of roles. The device may receive information associated with a set of tasks that is associated with the role, and compare the information associated with the set of tasks and information associated with a set of automation-capable tasks. The device may determine a set of automation scores associated with the set of tasks, and provide information associated with the set of automation scores to permit an action to be performed based on the set of automation scores.

Determination of task automation using natural language processing

A device may receive a plurality of data objects from a plurality of sources; identify text data, image data, and location data of the plurality of data objects; identify relevant data objects, of the plurality of data objects, based on the text data, and/or based on the image data, based on the location data, and/or based on comparing the text data, the image data, and the location data to a predefined element that identifies values relevant to a particular group or subject area; assign scores to the relevant data objects based on the text data, the image data, and the location data; aggregate the scores, as one or more aggregated scores, with regard to one or more users associated with the relevant data objects; and/or perform one or more actions based on the one or more aggregated scores associated with the one or more users.

Scoring mechanism for discovery of extremist content

Growing community of inventors

Dublin, Ireland

Anthony McCoy