Andeep S Toor

US Patent:

20190171726, Jun 6, 2019

Filed:

Dec 1, 2017

Appl. No.:

15/829601

Inventors:

- ARMONK NY, US
Charles E. Beller - Baltimore MD, US
WILLIAM G. DUBYAK - Severna Park MD, US
Palani Sakthi - Palatine IL, US
Kristen M. Summers - Takoma Park MD, US
Andeep S. Toor - Chantilly VA, US

International Classification:

G06F 17/30
G06N 99/00

Abstract:

An embodiment of the invention may include a method, computer program product and system for generating follow-up questions based on machine learning utilizing a computing device. The embodiment may include receiving an input question from a user. The embodiment may include parsing the received input question to extract input question components. Parsing utilizes natural language processing techniques. The embodiment may include executing trained question component models to predict follow-up question components. The extracted input question components are utilized as inputs to the trained question component models. The embodiment may include combining the predicted follow-up question components to generate one or more follow-up questions. The embodiment may include returning the one or more follow-up questions to the user.

US Patent:

20180270269, Sep 20, 2018

Filed:

May 22, 2018

Appl. No.:

15/986291

Inventors:

- Armonk NY, US
Aaron K. Baughman - Silver Spring MD, US
Nicholas A. McCrory - Sacramento CA, US
Andeep S. Toor - Chantilly VA, US
Michelle Welcks - Tampa FL, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

H04L 29/06
G06F 17/30

Abstract:

A set and a second set of collections of forecasted feature vectors are selected from a repository for a future time window, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set and a collection in the second set indicating an event related to the cyber-attack in a first region and a second event in a second region, respectively, of the environment at a discrete time. The events corresponding to the collections are classified, using an LTSM network, into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase that corresponds to the class is predicted as likely to occur during the future time window in the region.

US Patent:

20170316285, Nov 2, 2017

Filed:

Apr 28, 2016

Appl. No.:

15/140597

Inventors:

- Armonk NY, US
Andeep S. Toor - Chantilly VA, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06K 9/66
G06K 9/52
G06K 9/62
G06K 9/62
G06K 9/62
G06K 9/46

Abstract:

A transformed image is received. The transformed image includes an other-than-visible light image that has been captured using a transformation device. A region of the transformed image is isolated, the region being less than an entirety of the transformed image. By applying to the region a convolutional Neural Network (CNN) which executes using a processor and a memory, and by processing only the region of the transformed image, an object of interest is detected in the region. Upon detecting, an indication is produced to indicate the presence of the object of interest in the region.

US Patent:

20170230408, Aug 10, 2017

Filed:

Feb 9, 2016

Appl. No.:

15/019334

Inventors:

- Armonk NY, US
Aaron K. Baughman - Silver Spring MD, US
Nicholas A. McCrory - Sacramento CA, US
Andeep S. Toor - Chantilly VA, US
Michelle Welcks - Tampa FL, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

H04L 29/06

Abstract:

A set of collections of forecasted feature vectors is selected from a repository for a future time window after a present time, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set having feature vectors that are indicative of an event related to the cyber-attack in a region of the environment at a discrete time. The events corresponding to the collections in the set are classified into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase is determined that corresponds to the class. The determined phase is predicted as likely to occur during the future time window in the region.

US Patent:

20170230409, Aug 10, 2017

Filed:

Feb 9, 2016

Appl. No.:

15/019352

Inventors:

- Armonk NY, US
Aaron K. Baughman - Silver Spring MD, US
Nicholas A. McCrory - Sacramento CA, US
Andeep S. Toor - Chantilly VA, US
Michelle Welcks - Tampa FL, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

H04L 29/06
G06F 17/30

Abstract:

A set and a second set of collections of forecasted feature vectors are selected from a repository for a future time window, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set and a collection in the second set indicating an event related to the cyber-attack in a first region and a second event in a second region, respectively, of the environment at a discrete time. The set of collections is input at a first input and the second set of collections is input at a second input in the LSTM. The events corresponding to the collections are classified into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase that corresponds to the class is predicted as likely to occur during the future time window in the region.