Patents by Inventor David James Caira

David James Caira has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10339181
    Abstract: Various embodiments are generally directed to techniques for visualizing clustered datasets, such as by utilizing multiple colors and multiple color gradients to represent data from different clustered datasets, for instance. Some embodiments are particularly directed to using different colors associated with each cluster of data to visualize which cluster is dominant in each cell of a heat map. Further, in many embodiments, a color gradient may be used among different heat map cells of a common color that correspond to a common cluster to visualize data distributions within each cluster of data represented in the heat map. In multiple embodiments, colors and color gradients may be utilized in conjunction with visualizing clustered datasets to enable identification of useful patterns and relationships among a collection of clustered datasets. In several embodiments, heat maps and/or heat map matrices may be generated and presented via a user interface (UI).
    Type: Grant
    Filed: December 27, 2018
    Date of Patent: July 2, 2019
    Assignee: SAS INSTITUTE INC.
    Inventors: Rajendra Prasad Singh, Jordan Riley Benson, Xiangxiang Meng, David James Caira
  • Patent number: 10324983
    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.
    Type: Grant
    Filed: September 21, 2018
    Date of Patent: June 18, 2019
    Assignees: SAS INSTITUTE INC., NORTH CAROLINA STATE UNIVERSITY
    Inventors: Samuel Paul Leeman-Munk, Saratendu Sethi, Christopher Graham Healey, Shaoliang Nie, Kalpesh Padia, Ravinder Devarajan, David James Caira, Jordan Riley Benson, James Allen Cox, Lawrence E. Lewis
  • Publication number: 20190034558
    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.
    Type: Application
    Filed: September 21, 2018
    Publication date: January 31, 2019
    Applicants: SAS Institute Inc., North Carolina State University
    Inventors: SAMUEL PAUL LEEMAN-MUNK, SARATENDU SETHI, CHRISTOPHER GRAHAM HEALEY, SHAOLIANG NIE, KALPESH PADIA, RAVINDER DEVARAJAN, DAVID JAMES CAIRA, JORDAN RILEY BENSON, JAMES ALLEN COX, LAWRENCE E. LEWIS
  • Patent number: 10192001
    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.
    Type: Grant
    Filed: October 4, 2017
    Date of Patent: January 29, 2019
    Assignees: SAS INSTITUTE INC., NORTH CAROLINA STATE UNIVERSITY
    Inventors: Samuel Paul Leeman-Munk, Saratendu Sethi, Christopher Graham Healey, Shaoliang Nie, Kalpesh Padia, Ravinder Devarajan, David James Caira, Jordan Riley Benson, James Allen Cox, Lawrence E. Lewis, Mustafa Onur Kabul
  • Patent number: 10048826
    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.
    Type: Grant
    Filed: October 3, 2017
    Date of Patent: August 14, 2018
    Assignees: SAS INSTITUTE INC., NORTH CAROLINA STATE UNIVERSITY
    Inventors: Samuel Paul Leeman-Munk, Saratendu Sethi, Christopher Graham Healey, Shaoliang Nie, Kalpesh Padia, Ravinder Devarajan, David James Caira, Jordan Riley Benson, James Allen Cox, Lawrence E. Lewis, Mustafa Onur Kabul
  • Publication number: 20180096241
    Abstract: Deep neural networks can be visualized. For example, first values for a first layer of nodes in a neural network, second values for a second layer of nodes in the neural network, and/or third values for connections between the first layer of nodes and the second layer of nodes can be received. A quilt graph can be output that includes (i) a first set of symbols having visual characteristics representative of the first values and representing the first layer of nodes along a first axis; (ii) a second set of symbols having visual characteristics representative of the second values and representing the second layer of nodes along a second axis; and/or (iii) a matrix of blocks between the first axis and the second axis having visual characteristics representative of the third values and representing the connections between the first layer of nodes and the second layer of nodes.
    Type: Application
    Filed: May 2, 2017
    Publication date: April 5, 2018
    Inventors: CHRISTOPHER GRAHAM HEALEY, SHAOLIANG NIE, KALPESH PADIA, RAVINDER DEVARAJAN, DAVID JAMES CAIRA, JORDAN RILEY BENSON, SARATENDU SETHI, JAMES ALLEN COX, LAWRENCE E. LEWIS, SAMUEL PAUL LEEMAN-MUNK
  • Publication number: 20180095632
    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.
    Type: Application
    Filed: October 3, 2017
    Publication date: April 5, 2018
    Applicants: SAS Institute Inc., North Carolina State University
    Inventors: SAMUEL PAUL LEEMAN-MUNK, SARATENDU SETHI, CHRISTOPHER GRAHAM HEALEY, SHAOLIANG NIE, KALPESH PADIA, RAVINDER DEVARAJAN, DAVID JAMES CAIRA, JORDAN RILEY BENSON, JAMES ALLEN COX, LAWRENCE E. LEWIS, MUSTAFA ONUR KABUL
  • Publication number: 20180096078
    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.
    Type: Application
    Filed: October 4, 2017
    Publication date: April 5, 2018
    Applicants: SAS Institute Inc., North Carolina State University
    Inventors: Samuel Paul Leeman-Munk, Saratendu Sethi, Christopher Graham Healey, Shaoliang Nie, Kalpesh Padia, Ravinder Devarajan, David James Caira, Jordan Riley Benson, James Allen Cox, Lawrence E. Lewis, Mustafa Onur Kabul
  • Patent number: 9934462
    Abstract: Deep neural networks can be visualized. For example, first values for a first layer of nodes in a neural network, second values for a second layer of nodes in the neural network, and/or third values for connections between the first layer of nodes and the second layer of nodes can be received. A quilt graph can be output that includes (i) a first set of symbols having visual characteristics representative of the first values and representing the first layer of nodes along a first axis; (ii) a second set of symbols having visual characteristics representative of the second values and representing the second layer of nodes along a second axis; and/or (iii) a matrix of blocks between the first axis and the second axis having visual characteristics representative of the third values and representing the connections between the first layer of nodes and the second layer of nodes.
    Type: Grant
    Filed: May 2, 2017
    Date of Patent: April 3, 2018
    Assignee: SAS INSTITUTE INC.
    Inventors: Christopher Graham Healey, Samuel Paul Leeman-Munk, Shaoliang Nie, Kalpesh Padia, Ravinder Devarajan, David James Caira, Jordan Riley Benson, Saratendu Sethi, James Allen Cox, Lawrence E. Lewis
  • Patent number: 9704097
    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.
    Type: Grant
    Filed: December 11, 2015
    Date of Patent: July 11, 2017
    Assignees: SAS INSTITUTE INC., NORTH CAROLINA STATE UNIVERSITY
    Inventors: Ravinder Devarajan, Jordan Riley Benson, David James Caira, Saratendu Sethi, James Allen Cox, Christopher G. Healey, Gowtham Dinakaran, Kalpesh Padia
  • Publication number: 20160350651
    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.
    Type: Application
    Filed: December 11, 2015
    Publication date: December 1, 2016
    Inventors: Ravinder Devarajan, Jordan Riley Benson, David James Caira, Saratendu Sethi, James Allen Cox, Christopher G. Healey, Gowtham Dinakaran, Kalpesh Padia
  • Publication number: 20160350644
    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.
    Type: Application
    Filed: December 11, 2015
    Publication date: December 1, 2016
    Inventors: Ravinder Devarajan, Jordan Riley Benson, David James Caira, Saratendu Sethi, James Allen Cox, Christopher G. Healey, Gowtham Dinakaran, Kalpesh Padia
  • Publication number: 20160350664
    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.
    Type: Application
    Filed: June 8, 2016
    Publication date: December 1, 2016
    Inventors: Ravinder Devarajan, Jordan Riley Benson, David James Caira, Saratendu Sethi, James Allen Cox, Christopher G. Healey, Gowtham Dinakaran, Kalpesh Padia, Shaoliang Nie