Patents by Inventor James Allen Cox

James Allen Cox 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: 10275479
    Abstract: Methods, processes and computer-program products are disclosed for use in a parallelized computing system in which representations of large sparse matrices are efficiently encoded and communicated between grid-computing devices. A sparse matrix can be encoded and stored as a collection of character strings wherein each character string is a Base64 encoded string representing the non-zero elements of a single row of the sparse matrix. On a per-row basis, non-zero elements can be identified by column indices and error correction metadata can be included. The resultant row data can be converted to IEEE 754 8-byte representations and then encoded into Base64 characters for storage as strings. These character strings of even very large-dimensional sparse matrices can be efficiently stored in databases or communicated to grid-computing devices.
    Type: Grant
    Filed: February 27, 2015
    Date of Patent: April 30, 2019
    Assignee: SAS INSTITUTE INC.
    Inventors: Zheng Zhao, James Allen Cox, Russell Albright
  • 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
  • Publication number: 20180299369
    Abstract: A non-dispersive photoacoustic gas detector comprises an infrared light source, a closed chamber, a gas sample disposed within the closed chamber, and an acoustic sensor in fluid communication with the dosed chamber. The dosed chamber comprises at least one window that is substantially transparent to infrared light from the infrared light source, and the gas sample comprises a gas or a mixture of at least two gases.
    Type: Application
    Filed: September 7, 2016
    Publication date: October 18, 2018
    Inventors: Terry Marta, James Allen Cox, Bernard Fritz, Antony Phillips, Rodney Watts
  • Publication number: 20180284012
    Abstract: A non-dispersive photoacoustic gas detector includes an infrared light source, a first closed chamber, a first acoustic sensor in fluid communication with the first closed chamber, a second closed chamber, and a second acoustic sensor in fluid communication with the second closed chamber. The first closed chamber comprises a plurality of windows that are substantially transparent to infrared light from the infrared light source. The second closed chamber comprises at least one window that is substantially transparent to infrared light from the infrared light source, and the first closed chamber is arranged in series with the second closed chamber between the infrared light source and the second closed chamber.
    Type: Application
    Filed: September 7, 2016
    Publication date: October 4, 2018
    Applicant: Honeywell International Inc.
    Inventors: Terry Marta, James Allen Cox, Bernard Fritz, Antony Phillips, Rodney Watts
  • 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
  • Publication number: 20180045563
    Abstract: Embodiments relate generally to systems and methods for filtering unwanted wavelengths from an IR detector. In some embodiments, it may be desired to remove or reduce the wavelengths absorbed by water, to reduce the effects of water on the detection of the target gas. In some embodiments, a filter glass may be used in the IR detector, wherein the filter glass comprises one or more materials that contain hydroxyls in their molecular structure, and wherein the spectral absorption properties of the filter glass are operable to at least reduce wavelengths of light absorbed by water from the optical, thereby reducing the IR detector's cross sensitivity to water.
    Type: Application
    Filed: March 3, 2016
    Publication date: February 15, 2018
    Inventors: Terry Marta, Rodney Royston Watts, Antony Leighton Phillips, Bernard Fritz, James Allen Cox
  • 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
  • Patent number: 9595002
    Abstract: Electronic communications can be normalized using a neural network. For example, a noncanonical communication that includes multiple terms can be received. The noncanonical communication can be preprocessed by (I) generating a vector including multiple characters from a term of the multiple terms; and (II) repeating a substring of the term in the vector such that a last character of the substring is positioned in a last position in the vector. The vector can be transmitted to a neural network configured to receive the vector and generate multiple probabilities based on the vector. A normalized version of the noncanonical communication can be determined using one or more of the multiple probabilities generated by the neural network. Whether the normalized version of the noncanonical communication should be outputted can also be determined using at least one of the multiple probabilities generated by the neural network.
    Type: Grant
    Filed: June 7, 2016
    Date of Patent: March 14, 2017
    Assignee: SAS INSTITUTE INC.
    Inventors: Samuel Paul Leeman-Munk, James Allen Cox
  • Patent number: 9589166
    Abstract: A laser scanning system having a laser scanning field, and a laser beam optics module with an optical axis and including: an aperture stop disposed after a laser source for shaping the laser beam to a predetermined beam diameter; a collimating lens for collimating the laser beam produced from the aperture stop; an apodization element having a first and second optical surfaces for extending the depth of focus of the laser beam from the collimating lens; and a negative bi-prism, disposed after the apodization element, along the optical axis, to transform the energy distribution of the laser beam and cause the laser beam to converge to substantially a single beam spot along the far-field portion of the laser scanning field, and extend the depth of focus of the laser beam along the far-field portion of the laser scanning field.
    Type: Grant
    Filed: June 10, 2016
    Date of Patent: March 7, 2017
    Assignee: Metrologic Instruments, Inc.
    Inventors: Bernard Fritz, James Allen Cox, Peter L. Reutiman
  • Patent number: 9582761
    Abstract: Systems and methods for performing analyses on data sets to display canonical rules sets with dimensional targets are disclosed. A cross-corpus rule set for a given Topic can be generated based on the entire corpus of data. A first dimensional rule set can be generated based on a first context (e.g., based on the same Topic but using a first sub-domain of the corpus of data). A second dimensional rule set can be generated based on a second context (e.g., based on the same Topic but using a second sub-domain of the corpus of data). Key dimensional differentiators (e.g., for each dimension, or context, of the Topic) can be determined based on a comparison of the general rule set, the first dimensional rule set, and the second dimensional rule set. A canonical rule set visualization can be displayed. The visualization can highlight the dimensional selectors (e.g., those tokens, or nodes, that differ between the first dimensional rule set and the second dimensional rule set).
    Type: Grant
    Filed: May 8, 2015
    Date of Patent: February 28, 2017
    Assignee: SAS Institute Inc.
    Inventors: James Allen Cox, Barry De Ville, Zheng Zhao
  • Patent number: 9552547
    Abstract: Electronic communications can be normalized using neural networks. For example, an electronic representation of a noncanonical communication can be received. A normalized version of the noncanonical communication can be determined using a normalizer including a neural network. The neural network can receive a single vector at an input layer of the neural network and transform an output of a hidden layer of the neural network into multiple values that sum to a total value of one. Each value of the multiple values can be a number between zero and one and represent a probability of a particular character being in a particular position in the normalized version of the noncanonical communication. The neural network can determine the normalized version of the noncanonical communication based on the multiple values. Whether the normalized version should be output can be determined based on a result from a flagger including another neural network.
    Type: Grant
    Filed: November 10, 2015
    Date of Patent: January 24, 2017
    Assignees: SAS INSTITUTE INC., NORTH CAROLINA STATE UNIVERSITY
    Inventors: Samuel Paul Leeman-Munk, Wookhee Min, Bradford Wayne Mott, James Curtis Lester, II, James Allen Cox
  • Patent number: 9551614
    Abstract: Devices, methods, and systems for cavity-enhanced spectroscopy are described herein. One system includes an optical frequency comb (OFC) coupled to a laser source, and a cavity coupled to the OFC comprising a number of mirrors, wherein at least one of the number of mirrors is coupled to a piezo-transducer configured to alter a position of the at least one of the number of mirrors.
    Type: Grant
    Filed: December 19, 2014
    Date of Patent: January 24, 2017
    Assignee: Honeywell International Inc.
    Inventor: James Allen Cox
  • 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: 20160350650
    Abstract: Electronic communications can be normalized using neural networks. For example, an electronic representation of a noncanonical communication can be received. A normalized version of the noncanonical communication can be determined using a normalizer including a neural network. The neural network can receive a single vector at an input layer of the neural network and transform an output of a hidden layer of the neural network into multiple values that sum to a total value of one. Each value of the multiple values can be a number between zero and one and represent a probability of a particular character being in a particular position in the normalized version of the noncanonical communication. The neural network can determine the normalized version of the noncanonical communication based on the multiple values. Whether the normalized version should be output can be determined based on a result from a flagger including another neural network.
    Type: Application
    Filed: November 10, 2015
    Publication date: December 1, 2016
    Inventors: Samuel Paul Leeman-Munk, Wookhee Min, Bradford Wayne Mott, James Curtis Lester, II, James Allen Cox
  • Publication number: 20160350652
    Abstract: A neural network can be used to determine edit operations for normalizing an electronic communication. For example, an electronic representation of multiple characters that form a noncanonical communication can be received. It can be determined that the noncanonical communication is mapped to at least two canonical terms in a database. A recurrent neural network can be used to determine one or more edit operations usable to convert the noncanonical communication into a normalized version of the noncanonical communication. In some examples, the one or more edit operations can include inserting a character into the noncanonical communication, deleting the character from the noncanonical communication, or replacing the character with another character in the noncanonical communication. The noncanonical communication can be transformed into the normalized version of the noncanonical communication by performing the one or more edit operations.
    Type: Application
    Filed: December 14, 2015
    Publication date: December 1, 2016
    Inventors: Wookhee Min, Samuel Paul Leeman-Munk, Bradford Wayne Mott, James Curtis Lester, II, James Allen Cox