Christopher G Healey

US Patent:

20190034558, Jan 31, 2019

Filed:

Sep 21, 2018

Appl. No.:

16/138446

Inventors:

- Cary NC, US
- Raleigh NC, US
CHRISTOPHER GRAHAM HEALEY - Cary NC, US
SHAOLIANG NIE - Raleigh NC, US
KALPESH PADIA - Raleigh NC, US
RAVINDER DEVARAJAN - Cary NC, US
DAVID JAMES CAIRA - Chapel Hill NC, US
JORDAN RILEY BENSON - Ellerbe NC, US
JAMES ALLEN COX - Cary NC, US
LAWRENCE E. LEWIS - Raleigh NC, US

Assignee:

SAS Institute Inc. - Cary NC
North Carolina State University - Raleigh NC

International Classification:

G06F 17/30
G06F 3/0484
G06F 3/0481
G06K 9/62
G06N 3/04

Abstract:

Recurrent neural networks (RNNs) can be visualized. For example, a processor can receive vectors indicating values of nodes in a gate of a RNN. The values can result from processing data at the gate during a sequence of time steps. The processor can group the nodes into clusters by applying a clustering method to the values of the nodes. The processor can generate a first graphical element visually indicating how the respective values of the nodes in a cluster changed during the sequence of time steps. The processor can also determine a reference value based on multiple values for multiple nodes in the cluster, and generate a second graphical element visually representing how the respective values of the nodes in the cluster each relate to the reference value. The processor can cause a display to output a graphical user interface having the first graphical element and the second graphical element.

US Patent:

20180095632, Apr 5, 2018

Filed:

Oct 3, 2017

Appl. No.:

15/724029

Inventors:

- Cary NC, US
- Raleigh NC, US
CHRISTOPHER GRAHAM HEALEY - Cary NC, US
SHAOLIANG NIE - Raleigh NC, US
KALPESH PADIA - Raleigh NC, US
RAVINDER DEVARAJAN - Cary NC, US
DAVID JAMES CAIRA - Chapel Hill NC, US
JORDAN RILEY BENSON - Ellerbe NC, US
JAMES ALLEN COX - Cary NC, US
LAWRENCE E. LEWIS - Raleigh NC, US
MUSTAFA ONUR KABUL - Apex NC, US

Assignee:

SAS Institute Inc. - Cary NC
North Carolina State University - Raleigh NC

International Classification:

G06F 3/0481
G06N 3/04
G06T 11/60
G06F 9/44

Abstract:

Interactive visualizations of a convolutional neural network are provided. For example, a graphical user interface (GUI) can include a matrix having symbols indicating feature-map values that represent likelihoods of particular features being present or absent at various locations in an input to a convolutional neural network. Each column in the matrix can have feature-map values generated by convolving the input to the convolutional neural network with a respective filter for identifying a particular feature in the input. The GUI can detect, via an input device, an interaction indicating that that the columns in the matrix are to be combined into a particular number of groups. Based on the interaction, the columns can be clustered into the particular number of groups using a clustering method. The matrix in the GUI can then be updated to visually represent each respective group of columns as a single column of symbols within the matrix.

US Patent:

20180096078, Apr 5, 2018

Filed:

Oct 4, 2017

Appl. No.:

15/725026

Inventors:

- Cary NC, US
- Raleigh NC, US
Christopher Graham Healey - Cary NC, US
Shaoliang Nie - Raleigh NC, US
Kalpesh Padia - Raleigh NC, US
Ravinder Devarajan - Cary NC, US
David James Caira - Chapel Hill NC, US
Jordan Riley Benson - Ellerbe NC, US
James Allen Cox - Cary NC, US
Lawrence E. Lewis - Raleigh NC, US
Mustafa Onur Kabul - Apex NC, US

Assignee:

SAS Institute Inc. - Cary NC
North Carolina State University - Raleigh NC

International Classification:

G06F 17/30
G06N 3/04

Abstract:

Convolutional neural networks can be visualized. For example, a graphical user interface (GUI) can include a matrix of symbols indicating feature-map values that represent a likelihood of a particular feature being present or absent in an input to a convolutional neural network. The GUI can also include a node-link diagram representing a feed forward neural network that forms part of the convolutional neural network. The node-link diagram can include a first row of symbols representing an input layer to the feed forward neural network, a second row of symbols representing a hidden layer of the feed forward neural network, and a third row of symbols representing an output layer of the feed forward neural network. Lines between the rows of symbols can represent connections between nodes in the input layer, the hidden layer, and the output layer of the feed forward neural network.

US Patent:

20160350644, Dec 1, 2016

Filed:

Dec 11, 2015

Appl. No.:

14/966380

Inventors:

- Cary NC, US
- Raleigh NC, US
David James Caira - Chapel Hill NC, US
James Allen Cox - Cary NC, US
Christopher G. Healey - Cary NC, US
Gowtham Dinakaran - Raleigh NC, US
Kalpesh Padia - Raleigh NC, US

International Classification:

G06N 3/04

Abstract:

The results of electronic sentiment analysis can be visualized. For example, multiple sentiments expressed in an electronic communication can be determined using a neural network. Each sentiment of the multiple sentiments can include a positive sentiment, a neutral sentiment, or a negative sentiment. A transition between at least two sentiments of the multiple sentiments can be determined. The transition can indicate a change between the at least two sentiments occurring over a period of time. A graphical user interface visually indicating the transition between the at least two sentiments can be displayed on a timeline. The timeline can include a timeframe associated with multiple segments of the electronic communication.

US Patent:

20160350651, Dec 1, 2016

Filed:

Dec 11, 2015

Appl. No.:

14/966117

Inventors:

- Raleigh NC, US
- Cary NC, US
David James Caira - Chapel Hill NC, US
James Allen Cox - Cary NC, US
Christopher G. Healey - Cary NC, US
Gowtham Dinakaran - Raleigh NC, US
Kalpesh Padia - Raleigh NC, US

International Classification:

G06N 3/08
G06N 5/02

Abstract:

Training data for training a neural network usable for electronic sentiment analysis can be automatically constructed. For example, an electronic communication usable for training the neural network and including multiple characters can be received. A sentiment dictionary including multiple expressions mapped to multiple sentiment values representing different sentiments can be received. Each expression in the sentiment dictionary can be mapped to a corresponding sentiment value. An overall sentiment for the electronic communication can be determined using the sentiment dictionary. Training data usable for training the neural network can be automatically constructed based on the overall sentiment of the electronic communication. The neural network can be trained using the training data. A second electronic communication including an unknown sentiment can be received. At least one sentiment associated with the second electronic communication can be determined using the neural network.

US Patent:

20160350664, Dec 1, 2016

Filed:

Jun 8, 2016

Appl. No.:

15/177237

Inventors:

- Cary NC, US
- Raleigh NC, US
David James Caira - Chapel Hill NC, US
James Allen Cox - Cary NC, US
Christopher G. Healey - Cary NC, US
Gowtham Dinakaran - Raleigh NC, US
Kalpesh Padia - Raleigh NC, US
Shaoliang Nie - Raleigh NC, US

International Classification:

G06N 5/04
G06F 17/27
G06F 17/30
G06N 99/00

Abstract:

The results of electronic narrative analytics can be visualized. For example, an electronic communication that includes multiple narratives can be received. Each narrative can be segmented into respective blocks of characters. Multiple sentiments associated with the respective blocks of characters can be determined. Multiple sentiment patterns can be determined based on the multiple sentiments. The multiple sentiment patterns can be categorized into multiple sentiment pattern groups. Also, multiple semantic tags associated with the multiple sentiment patterns can be determined. Further, the multiple narratives can be categorized into multiple topic sets. A graphical user interface can be displayed visually indicating at least a portion of: the multiple sentiments, the multiple sentiment pattern groups, the multiple semantic tags, or the multiple topic sets.