Altria Client Services LLC

Capital One Services, LLC

Philip Morris Products S.a.

Catherine Barnes

Christopher S Tucker

Munmaya K Mishra

Georgios D Karles

Peter J Lipowicz

Ali A Rostami

Gerd Kobal

David B Kane

Jason W Flora

Yezdi B Pithawalla

Richard Arena

Matthew Louis Nowak

David Walter Peters

Kent Brian Koller

Narender Pashikant

George Karles

Keith D Greene

Maqbool A Khatri

Jennifer Honermann

Peter Todd Akers

Kelvin Wong

James C McGRORY

Rees Draminski

Justin Cheng

Sean O'neill

Alan Christopher Weaver

In some implementations, a data quality system may receive an incoming data set for data quality analysis. The data quality system may obtain data quality validation information from one or more data repositories that form a data quality knowledge base. The data quality system may use one or more machine learning models to evaluate whether the incoming data set has one or more anomalies based on the data quality validation information. The data quality system may generate data quality results for the incoming data set based on an output from the one or more machine learning models. The data quality system may update the data quality knowledge base in accordance with the data quality results for the incoming data set.

Artificial intelligence system for data quality resolution

A base for an e-vaping device is configured to couple with multiple cartridges configured to generate separate, respective dispersions. The cartridges may include one or more atomizer assemblies or vaporizer assemblies. The base may include multiple connectors electrically coupled to the power supply. The connectors may be configured to couple multiple dispersion generators to a power supply of the base. The base may include control circuitry configured to independently control dispersion generation by dispersion generators coupled to the base. The control circuitry may independently control dispersion generation by the first and second cartridges based on cartridge information accessed through at least one of the first and second connectors. The control circuitry may control dispersion generation by controlling power supplied to the dispersion generators.

Multiple dispersion generator e-vaping device

A base for an e-vaping device is configured to couple with multiple cartridges configured to generate separate, respective dispersions. The cartridges may include one or more atomizer assemblies or vaporizer assemblies. The base may include multiple connectors electrically coupled to the power supply. The connectors may be configured to removably couple with various cartridges. Different cartridges may be interchangeably coupled with the connectors. Different cartridges may be swapped from the connectors. The base may include control circuitry configured to independently control dispersion generation by cartridges coupled to the base. The control circuitry may control dispersion generation by controlling power supplied to the cartridges. The control circuitry may control electrical power supplied based on information accessed from one or more of the cartridges.

E-vaping device cartridge holder

A method, apparatus, and computer-readable medium are described that identify subject matter of text using identified groups and a machine-learning model. Using the combination of the identified subject matter and the machine-learning model, classifications may be adjusted over time. Based on the adjusted classifications, the machine-learning model may be retrained to better classify previously unclassified text. One or more benefits may include better summarization of text documents and/or better training of machine-learning models that are then used to assist in the summarization of the text documents. The resulting classifications may be used to improve resource allocations for future tasks.

Text classification via term mapping and machine-learning classification model

Aspects described herein may use machine learning models to predict one or more remediation actions to mitigate occurrence of an incident based upon previous incidents of an entity. Asset ownership data and development operations tools metric data are compiled and a relationship between the compiled data and an occurrence of previous incidents is determined. A machine learning model predicts relationships between the occurrence of a previous incident and assets data. One or more remediation actions are assigned to an asset and a notification is outputted regarding the same.

Predictive remediation action system

Aspects described herein may use machine learning models to predict one or more remediation actions to mitigate reoccurrence of an incident that has become restored based upon previous incidents of an entity. Historical incident data is compiled into two incident datasets: one representative of incidents that were assigned a remediation action to mitigate reoccurrence of the incident, and a second representative of incidents that were not assigned a remediation action. A machine learning model matches relationships between data in the two datasets and outputs scores representative of similarities. Based on the scores, one or more remediation actions are mapped to an incident in the second dataset and the remediation action is performed for the incident.

Remediation action system

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Richmond, VA, United States of America

Catherine Barnes