Accenture Global Solutions Limited

Accenture Global Services Gmbh

Andrew John Truscott

Teresa Sheausan Tung

Dennis Carroll

Ethan Bischoff

German Acuna

Christopher Donnelly

Jean M Becker

Cecil O'Dell Lynch

Dennis James Carroll

Cecil O Lynch

Shawn D Roman

Scott D Myers

Cecil Lynch

Ethan R Bischoff

John F Quinn

William J Tronoski

Marshall Ruffin

Gale E Thompson

Brian J Kelly

John S Celi

Amy H Wright

Brandon Winful

Garret R Wu

Mallikarjun Bepeta

Cecil O'dell Lynch

In some implementations, a device may receive prescription information associated with a medication in a container. The device may cause a camera to capture first image data associated with the medication while the medication is in the container and the container is positioned on a receptacle. The device may cause an adjusting device to reposition the container on the receptacle. The device may cause the camera to capture second image data associated with the medication while the medication is in the container. The device may process, via a neural network, the first image data and the second image data to identify the medication based on depictions of individual units of the medication included in the first image data and the second image data. The device may verify the medication based on the prescription information and an identifier of the medication provided by the neural network.

Identification and verification of medication

A device obtains prescription information relating to a medication in a container. The device causes a camera device of the device to obtain image data relating to the medication and a weighing device of the device to obtain weight data relating the medication. The device sends the prescription information, the image data, and the weight data to a different device to cause the different device to verify the medication using a machine learning model. The device receives information concerning the medication and automatically generates, based on the information concerning the medication, a message concerning the medication, wherein the message includes instructions on how much of the medication a user of the device is to take. The device causes the device or an additional device to present the message.

A device may receive one or more images that depict one or more shapes associated with a first object that includes a portal. The device may identify, based on the one or more images, one or more characteristics of the one or more shapes. The device may determine, based on the one or more characteristics of the one or more shapes, one or more attributes of the portal. The device may determine, based on the one or more attributes of the portal, positioning information to be used to position a second object relative to the portal of the first object. The device may output, based on the positioning information, one or more control signals associated with positioning the second object relative to the portal.

Devices, systems, and methods for automated loading bridge positioning using shapes associated with a vehicle

A device may obtain, from one or more cameras, an image that depicts one or more encoded decals positioned on a body of a vehicle. The device may process the image and may identify, based on processing the image, one or more characteristics of the one or more encoded decals. The device may determine, based on the one or more characteristics of the one or more encoded decals, positioning information to be used to position a loading bridge relative to the body of the vehicle. The device may determine one or more control signals based on the positioning information. The device may provide the one or more control signals to one or more actuators to automatically position the loading bridge relative to the body of the vehicle.

Automated loading bridge positioning using encoded decals

A patient anonymizing system includes a plurality of hashing appliances and data sources. Each appliance receives medical records containing at least confidential protected health information (PHI). A salt value is appended to each confidential PHI, and a hash is generated, which replaces the confidential PHI to generate an anonymized record. A master patient index server aggregates the anonymized records. A vector and cluster matching engine determines if the anonymized record matches a unique patient identifier corresponding to a second anonymized record. A comparison vector is generated by comparing hash values of the confidential PHI with hash values in the second anonymized record, and is crossed with a confidence vector having weights based on match conditions. This produces a match confidence level, which is compared to a threshold. If the threshold is met, the anonymized record is mapped to the unique patient identifier associated with the second record.

System for anonymizing and aggregating protected health information

A system for anonymizing and aggregating protected information (PI) from a plurality of data sources includes a master index server coupled to a data repository. The master index server receives an anonymized records associated with an individual from a plurality of data hashing appliances. The system includes a cluster matching engine that applies a plurality of rules to hashed data elements of the received record for comparing hashed data elements of the record with hashed data elements of a plurality of clusters of anonymized records associated with different individuals stored in the data repository to determine whether the individual associated with the received record corresponds to an individual associated with one of the clusters of anonymized records. When a match is found, the cluster matching engine adds the received record to the cluster of anonymized records associated with that individual.

System for anonymizing and aggregating protected information

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Spring, TX, United States of America

Andrew John Truscott