Kevin N Tran

US Patent:

20200278863, Sep 3, 2020

Filed:

May 20, 2020

Appl. No.:

16/878707

Inventors:

- Armonk NY, US
ITALO BULEJE - Orlando FL, US
Gigi Y.C. Yuen-Reed - Tampa FL, US
Shilpa N. Mahatma - Chappaqua NY, US
Kevin N. Tran - Marina del Rey CA, US

International Classification:

G06F 8/76

Abstract:

Embodiments of the present invention disclose a method, a computer program product, and a computer system for mapping components of non-distributed environments to distributed environments. A computer receives a data pipeline configured for a non-distributed environment and identifies one or more bottleneck components of the data pipeline. In addition, the computer converts data used in the pipeline to a format compatible with a distributed environment and installs the necessary computing libraries necessary for operating the pipeline within the distributed environment. The computer further converts the code of the pipeline to a code that is compatible with the distributed environment and optimizes components of the pipeline for use in the distributed environment.

US Patent:

20200251205, Aug 6, 2020

Filed:

Jan 31, 2019

Appl. No.:

16/263601

Inventors:

- Armonk NY, US
Gigi Y. C. Yuen-Reed - Tampa FL, US
Huijing Jiang - Chappaqua NY, US
Kevin N. Tran - Marina del Rey CA, US
Bob Kelley - Ann Arbor MI, US
Thomas Halvorson - Saline MI, US

International Classification:

G16H 40/20
G06Q 10/06
G06N 20/00

Abstract:

Computer-implemented methods, systems, and computer readable media are provided for identifying and optimizing performance drivers of a healthcare related system. Healthcare related data may be analyzed to produce performance information pertaining to performance indicators for performance drivers of the healthcare related system. From the performance information, changes in the sets of performance indicators over time for the performance drivers may be determined and performance drivers with determined changes satisfying a threshold may be identified. An impact of the determined changes in the performance indicators to the identified performance drivers and contributions to the determined impact from one or more factors may be identified. Factors of the identified performance drivers with opposing utilization trends may be identified and an impact of the identified factors on the performance drivers may be determined. The identified performance drivers may be ranked and use of the performance drivers may be adjusted based on the ranking.

US Patent:

20190324745, Oct 24, 2019

Filed:

Apr 20, 2018

Appl. No.:

15/957962

Inventors:

- ARMONK NY, US
ITALO BULEJE - Orlando FL, US
Gigi Y.C. Yuen-Reed - Tampa FL, US
Shilpa N. Mahatma - Chappaqua NY, US
Kevin N. Tran - Boston MA, US

International Classification:

G06F 8/76

Abstract:

Embodiments of the present invention disclose a method, a computer program product, and a computer system for mapping components of non-distributed environments to distributed environments. A computer receives a data pipeline configured for a non-distributed environment and identifies one or more bottleneck components of the data pipeline. In addition, the computer converts data used in the pipeline to a format compatible with a distributed environment and installs the necessary computing libraries necessary for operating the pipeline within the distributed environment. The computer further converts the code of the pipeline to a code that is compatible with the distributed environment and optimizes components of the pipeline for use in the distributed environment.

US Patent:

20190197431, Jun 27, 2019

Filed:

Dec 27, 2017

Appl. No.:

15/854839

Inventors:

- Armonk NY, US
Kevin N. Tran - Boston MA, US
Fang Lu - Billerica MA, US
Gigi Y. C. Yuen-Reed - Tampa FL, US

International Classification:

G06N 99/00
G06F 17/30

Abstract:

A bioinformatics system for autonomous hypothesis generation for relatedness of seemingly independent concepts having tenuous or no prior known relationship in a knowledgebase. Independent concept pairs are identified in a question, and they are evaluated in light of lists of their similar concepts, and in light input documents, word embeddings, and co-occurrence matrices that are organized as a time series. Relationships between the independent concepts are hypothesized based on temporal analysis of relatedness measures of their similar concepts, measured via candidate connecting concepts.

US Patent:

20190179943, Jun 13, 2019

Filed:

Dec 8, 2017

Appl. No.:

15/835824

Inventors:

- Armonk NY, US
Shilpa N. Mahatma - Chappaqua NY, US
RACHITA CHANDRA - Cambridge MA, US
Kevin N. Tran - Boston MA, US
Dennis Wei - White Plains NY, US
Karthikeyan NATESAN RAMAMURTHY - Culver City CA, US
Gigi Yuen-Reed - Tampa FL, US

International Classification:

G06F 17/30
G06F 9/38
G06F 8/38

Abstract:

Modularized data processing systems and methods for its use are provided. Processing a current job can reuse data generated for a previously processed job to the extent the two share parameter configurations. Similarly, outputs of processing modules generated during processing the previously processed job can be used as inputs to processing modules processing a current job, if the two jobs share some parameter configurations.

US Patent:

20190172564, Jun 6, 2019

Filed:

Dec 5, 2017

Appl. No.:

15/831426

Inventors:

- Armonk NY, US
Vijay S. Iyengar - Cortlandt Manor NY, US
Dmitriy A. Katz - Ossining NY, US
Karthikeyan Natesan Ramamurthy - Culver City CA, US
Emily A. Ray - Hastings on Hudson NY, US
Moninder Singh - Farmington CT, US
Dennis Wei - White Plains NY, US
Gigi Y. C. Yuen-Reed - Tampa FL, US
Kevin N. Tran - Boston MA, US

International Classification:

G16H 10/60
G16H 50/30
G06N 99/00
G06F 17/30
G06N 5/02

Abstract:

A system may predict costs for a set of members by building and using a predictive pipeline. The pipeline may be built using a set of historical data for training members. A set of member-level features can be identified by performing empirical testing on the set of historical data. The trained configurable predictive pipeline can generate a set of predictive data for each member, using historical test data for a set of testing members. The system can then generate a predictive report for each set of predictive data.