Accenture Global Solutions Limited

Accenture Global Services Gmbh

Qurrat Ul Ain

Anthony McCoy

Gaurav Tandon

Rexall E Thexton

Sanjeev Shukla

Andrew Poole

Colleen Connolly

Sidath Mudiyanselage

Hannah Craddock

Farbod Kamiab

Nicholas McCarthy

Jeremiah Patrick Hayes

Freddy Lecue

Chahrazed Bouhini

Mykhaylo Zayats

Urvesh Bhowan

Bogdan Eugen Sacaleanu

Laura O'Malley

Navdeep Sharma

Aoife Whelan

Gavin Kearney

Christina Gunther

Harshdeep Harshdeep

Patrick Moreau

Jeremiah Hayes

Philip O'KELLY

Complex computer system architectures are described for utilizing a knowledge data graph comprised of elements, and selecting a discovery element to replace an existing element of a formulation depicted in the knowledge data graph. The substitution process takes advantage of the knowledge data graph structure to improve the computing capabilities of a computing device executing a substitution calculation by translating the knowledge data graph into an embedding space, and determining a discovery element from within the embedding space.

Compound discovery via information divergence with knowledge graphs

Knowledge graph systems are disclosed for enhancing a knowledge graph by generating a new node. The knowledge graph system converts a knowledge graph into an embedding space, and selects a region of interest from within the embedding space. The knowledge graph system further identifies, from the region of interest, one or more gap regions, and calculates a center for each gap region. A node is generated for each gap region, and the information represented by the node is added to the original knowledge graph to generate an updated knowledge graph.

Density-based computation for information discovery in knowledge graphs

The present disclosure relates to systems, methods, and products for identifying candidate molecule. The system includes a non-transitory memory storing instructions; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to receive drug data; convert the drug data into at least one point in a latent space using a grammar variational auto-encoder (VAE) model; receive a query for the at least one candidate molecule; select one or more points in the latent space; and create a k-dimensional tree graph based on the query for the at least one candidate molecule and the selected one or more points; determine a plurality of paths according to an interpolation technique; receive preference data; determine an optimum path; determine at least one candidate point on the optimum path; and determine a drug molecular structure using an inverse of the grammar VAE model.

Denovo generation of molecules using manifold traversal

A device may receive medical data associated with potential patients for a clinical trial of a drug, where the medical data includes one or more of multi-omics data associated with the potential patients, genomic profiles of the potential patients, dosage and time associated with the drug, electronic medical records of the potential patients, or clinical trial data associated with the drug. The device may perform, in parallel, a topological data analysis, a non-matrix factorization analysis, and a neural network analysis of the medical data. The device may identify a group of patients, of the potential patients, for the clinical trial of the drug based on performing the topological data analysis, the non-matrix factorization analysis, and the neural network analysis of the medical data, and may provide information identifying the group of patients.

Accelerated clinical biomarker prediction (ACBP) platform

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

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

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Dublin, Ireland

Qurrat Ul Ain