Jian X Lin

US Patent:

20130305396, Nov 14, 2013

Filed:

Sep 28, 2011

Appl. No.:

13/876232

Inventors:

Luigi Grasso - Bryn Mawr PA, US
Jian Min Lin - Malvern PA, US
Yuhong Zhou - Phoenixville PA, US
Brian E. Tomkowicz - East Norriton PA, US
Nicholas C. Nicolaides - Glen Mills PA, US
Philip M. Sass - Audubon PA, US

International Classification:

A61K 49/00

US Classification:

800 3, 800 18, 435354, 435 723

Abstract:

Provided herein are rodents that express the human endosialin gene. In preferred embodiments, the rodent is a mouse. Preferably, the human endosialin gene is integrated into the native or endogenous endosialin gene locus. More preferably, the host rodent is null for the endogenous endosialin gene product. The human endosialin gene is preferably expressed in a similar development and disease response pattern as that of the native endosialin gene product in parental or wild type rodents. This feature makes these rodents useful for studying the effects of test agents to positively or negatively affect endosialin biology for therapeutic use. Use of human endosialin expressing rodents lacking native endosialin gene product (HUE rodents) is proposed as a strategy for developing agents that can positively or negatively affect the endosialin pathway and also serve as a screening tool to identify those agents that may be useful as human therapies.

US Patent:

20190347578, Nov 14, 2019

Filed:

May 10, 2018

Appl. No.:

15/976463

Inventors:

- Armonk NY, US
Matthew Elsner - Dunwoody GA, US
Jian Lin - Alpharetta GA, US
Matthew Paul Ouellette - Fredericton, CA
Yun Pan - Roswell GA, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06N 99/00
H04L 29/06

Abstract:

A machine learning (ML)-based technique for user behavior analysis that detects when users deviate from expected behavior. A ML model is trained using training data derived from activity data from a first set of users. The model is refined in a computationally-efficient manner by identifying a second set of users that constitute a “watch list.” At a given time, a differential data ingestion operation is then performed to incorporate data for the second set of users into the training data, while also pruning at least a portion of the data set corresponding to data associated with any user included in the first set but not in the second set. These operations update the training data used for the machine learning. The machine learning model is then refined based on the updated training data that incorporates the activity data ingested from the users identified in the watch list.

Address:

167 E Broadway APT 15, New York, NY 10002

Phone:

(718)5515848

VIN:

4T1BF1FK9CU018463

Make:

TOYOTA

Model:

CAMRY

Year:

2012