Ping Zhang, 4913235 Sanford Ave, Flushing, NY 11355

D665916, Aug 21, 2012

Oct 3, 2011

29/403188

Ping Zhang - Port Washington NY, US
Ling Anderson - Forest Hills NY, US

Nefeli, Corp. - Port Washington NY

2803

D24215

2013006, Mar 14, 2013

Sep 14, 2012

13/618187

Ping Zhang - Port Washington NY, US

Nefeli Corp. - Port Washington NY

A61K 8/97
A61Q 19/08

424725

A topical skin care composition containing an extract solution of and a topical carrier.

2013016, Jun 27, 2013

Dec 21, 2012

13/723602

Ping Zhang - Port Washington NY, US

Nefeli Corp. - Port Washington NY

A61K 36/481
A61Q 19/08
A61K 8/97

424757

An aqueous seed extract solution prepared by a method including soaking dried seeds of seed in an aqueous solvent to form a suspension, boiling the suspension for at least 30 minutes, and filtering the suspension so as to obtain the seed aqueous extract solution, the weight ratio of the dried seeds of seed to the extract solution being 1:100 to 75:100.

2020035, Nov 12, 2020

May 7, 2019

16/405025

- Armonk NY, US
Ping Zhang - White Plains NY, US
Yoonyoung Park - Cambridge MA, US
Kenney Ng - Arlington MA, US
Fang Lu - Billerica MA, US
Michael J. LaScaleia - Sudbury MA, US
Francis Campion - Westwood MA, US

G16H 10/20
G16H 10/60
G06F 17/28

A data analysis system is configured for alerting to the results of a virtual clinical trial. The data analysis system has an extraction system configured to extract a correlation between an ingredient and an effect from a clinical trial document using natural language processing, and extract medical history information for a population exposed to the ingredient and a population unexposed to the ingredient from stored electronic medical records. The data analysis system also has a virtual trial system configured to perform statistical analysis on the medical history information to assess the level of association between the ingredient and the effect, a decision system configured to determine an accuracy score for the correlation based on the statistical analysis, and an alerting system configured to provide the accuracy score to a user interface of an end-user device.

2020005, Feb 13, 2020

Oct 16, 2019

16/654539

- Armonk NY, US
Ying LI - FORT LEE NJ, US
Zhaonan SUN - Elmsford NY, US
Ping ZHANG - White Plains NY, US

G16C 20/30
G06N 5/02
G06N 5/00

Predicting beneficial drug combinations mitigating adverse drug reactions identifies drug combinations and associated target adverse drug reaction from a spontaneous reporting system containing case reports of drugs and associated adverse drug reactions. Each drug combination comprises a first drug and a second drug, and a propensity score is computed for each drug in each group. This propensity score expresses a probability of being exposed to a given drug based on other co-prescribed drugs and reported indications, which reflect patient characteristics. Associations are computed for each drug as well as drug interaction. Among the associations, the sum of the associations of the second drug and the interaction effect represents the predicted beneficial score expressing whether the second drug alters the chance of developing the target adverse drug reaction for patients on the first drug. The interaction effect is referred to as predicted interaction score, and represents antagonistic or synergistic drug interactions.

2019038, Dec 19, 2019

Jun 18, 2018

16/011231

- Armonk NY, US
Zach Shahn - Brooklyn NY, US
Ping Zhang - White Plains NY, US
Fei Wang - Ossining NY, US
Jianying Hu - Bronx NY, US

G06F 19/12
G06F 19/18
G16H 50/30
G16H 10/60

Techniques are described that facilitate determining potential cancer gene therapy targets by joint modeling of cancer survival events. In one embodiment, a computer-implemented comprises employing, by a device operatively coupled to a processor, a multi-task learning model to determine active genetic factors respectively associated with different types of cancer based on cancer survival data and patient genomic data for groups of patients that respectively survived the different types of cancer. The computer-implemented method further comprises, determining, by the device, common active genetic factors of the active genetic factors that are shared between two or more types of cancer of the different types of cancer.

2019030, Oct 3, 2019

Apr 3, 2018

15/943773

- Armonk NY, US
YINGKAI Gao - White Plains NY, US
HENG LUO - Ossining NY, US
PING ZHANG - White Plains NY, US
Sanjoy Dey - White Plains NY, US

G06F 19/24
G06N 5/04
G06N 7/00
G06N 3/08
G06F 17/30
G06N 3/04
G06N 99/00
G06F 17/16

Link prediction for biomedical entities. A neural network is trained using known associations between biomedical entities, including their vector representations and additional information-carrying content describing the biomedical entities. The trained network infers or predicts unobserved associations between two entities.

2019028, Sep 19, 2019

Mar 13, 2018

15/920290

- Armonk NY, US
Heng Luo - Ossining NY, US
Wendy Dawn Cornell - Warren NJ, US
Ping Zhang - White Plains NY, US

G06F 19/00
G06F 17/18
G06N 5/04
G06N 99/00

Techniques regarding efficient means for chemical compound discovery are provided. For example, one or more embodiments can regard a system, which can comprise a memory that stores computer executable components and a processor, operably coupled to the memory, that can execute the computer executable components stored in the memory. The computer executable components can comprise a test component that can determine a first parameter value of a tested chemical compound from a plurality of chemical compounds. Additionally, a model component can generate a regression analysis model using a value information analysis. The regression analysis model can regard the plurality of chemical compounds based on the first parameter value. Further, an identification component can identify a preferred chemical compound from the plurality of chemical compounds based on the regression analysis model. A second parameter value of the preferred chemical compound can be greater than a defined threshold.