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: 11106694
    Abstract: Computerized pipelines can transform input data into data structures compatible with models in some examples. In one such example, a system can obtain a first table that includes first data referencing a set of subjects. The system can then execute a sequence of processing operations on the first data in a particular order defined by a data-processing pipeline to modify an analysis table to include features associated with the set of subjects. Executing each respective processing operation in the sequence to generate the modified analysis table may involve: deriving a respective set of features from the first data by executing a respective feature-extraction operation on the first data; and adding the respective set of features to the analysis table. The system may then execute a predictive model on the modified analysis table for generating a predicted value based on the modified analysis table.
    Type: Grant
    Filed: February 11, 2021
    Date of Patent: August 31, 2021
    Assignee: SAS INSTITUTE INC.
    Inventors: James Allen Cox, Nancy Anne Rausch
  • Publication number: 20210263949
    Abstract: Computerized pipelines can transform input data into data structures compatible with models in some examples. In one such example, a system can obtain a first table that includes first data referencing a set of subjects. The system can then execute a sequence of processing operations on the first data in a particular order defined by a data-processing pipeline to modify an analysis table to include features associated with the set of subjects. Executing each respective processing operation in the sequence to generate the modified analysis table may involve: deriving a respective set of features from the first data by executing a respective feature-extraction operation on the first data; and adding the respective set of features to the analysis table. The system may then execute a predictive model on the modified analysis table for generating a predicted value based on the modified analysis table.
    Type: Application
    Filed: February 11, 2021
    Publication date: August 26, 2021
    Applicant: SAS Institute Inc.
    Inventors: James Allen Cox, Nancy Anne Rausch
  • Patent number: 11074412
    Abstract: A system trains a classification model. Text windows are defined from tokens based on a window size. A network model including a transformer network is trained with the text windows to define classification information. A first accuracy value is computed. (A) The window size is reduced using a predefined reduction factor value. (B) Second text windows are defined based on the reduced window size. (C) Retrain the network model with the second text windows to define classification information. (D) A second accuracy value is computed. (E) An accuracy reduction value is computed from the second accuracy value relative to the first accuracy value. When the computed accuracy reduction value is ?an accuracy reduction tolerance value, repeat (A)-(E) until the accuracy reduction value is <the accuracy reduction tolerance value. Otherwise, increase the window size, define final text windows based on the increased window size, and retrain the network model.
    Type: Grant
    Filed: March 16, 2021
    Date of Patent: July 27, 2021
    Assignee: SAS Institute Inc.
    Inventors: Samuel Paul Leeman-Munk, James Allen Cox, David Blake Styles, Richard Welland Crowell
  • Patent number: 11048884
    Abstract: A computing system receives a collection comprising multiple sets of ordered terms, including a first set. The system generates a dataset indicating an association between each pair of terms within a same set of the collection by generating co-occurrence score(s) for the first set. The system generates computed probabilities based on the co-occurrence score(s) for the first set. The computed probabilities indicate a likelihood that one term in a given pair of terms of the collection appears in a given set of the collection given that another term in the given pair of terms of the collection occurs. The system smoothes the computed probabilities by adding one or more random observations. The system generates one or more association indications for the first set based on the smoothed computed probabilities. The system outputs an indication of the dataset. Additionally, or alternatively, based on association measure(s), the system generates a virtual term.
    Type: Grant
    Filed: October 1, 2020
    Date of Patent: June 29, 2021
    Assignee: SAS Institute Inc.
    Inventors: James Allen Cox, Russell Albright, Saratendu Sethi
  • Publication number: 20210027024
    Abstract: A computing system receives a collection comprising multiple sets of ordered terms, including a first set. The system generates a dataset indicating an association between each pair of terms within a same set of the collection by generating co-occurrence score(s) for the first set. The system generates computed probabilities based on the co-occurrence score(s) for the first set. The computed probabilities indicate a likelihood that one term in a given pair of terms of the collection appears in a given set of the collection given that another term in the given pair of terms of the collection occurs. The system smoothes the computed probabilities by adding one or more random observations. The system generates one or more association indications for the first set based on the smoothed computed probabilities. The system outputs an indication of the dataset. Additionally, or alternatively, based on association measure(s), the system generates a virtual term.
    Type: Application
    Filed: October 1, 2020
    Publication date: January 28, 2021
    Inventors: James Allen Cox, Russell Albright, Saratendu Sethi
  • Patent number: 10883875
    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: Grant
    Filed: March 3, 2016
    Date of Patent: January 5, 2021
    Assignee: HONEYWELL INTERNATIONAL INC.
    Inventors: Terry Marta, Rodney Royston Watts, Antony Leighton Phillips, Bernard Fritz, James Allen Cox
  • Patent number: 10860809
    Abstract: A computing system receives a collection comprising multiple sets of ordered terms, including a first set. The system generates a dataset indicating an association between each pair of terms within a same set of the collection by generating co-occurrence score(s) for the first set. The system generates computed probabilities based on the co-occurrence score(s) for the first set. The computed probabilities indicate a likelihood that one term in a given pair of terms of the collection appears in a given set of the collection given that another term in the given pair of terms of the collection occurs. The system smoothes the computed probabilities by adding one or more random observations. The system generates one or more association indications for the first set based on the smoothed computed probabilities. The system outputs an indication of the dataset. Additionally, or alternatively, based on association measure(s), the system generates a virtual term.
    Type: Grant
    Filed: April 2, 2020
    Date of Patent: December 8, 2020
    Assignee: SAS Institute Inc.
    Inventors: James Allen Cox, Russell Albright, Saratendu Sethi
  • Publication number: 20200327285
    Abstract: A computing system receives a collection comprising multiple sets of ordered terms, including a first set. The system generates a dataset indicating an association between each pair of terms within a same set of the collection by generating co-occurrence score(s) for the first set. The system generates computed probabilities based on the co-occurrence score(s) for the first set. The computed probabilities indicate a likelihood that one term in a given pair of terms of the collection appears in a given set of the collection given that another term in the given pair of terms of the collection occurs. The system smoothes the computed probabilities by adding one or more random observations. The system generates one or more association indications for the first set based on the smoothed computed probabilities. The system outputs an indication of the dataset. Additionally, or alternatively, based on association measure(s), the system generates a virtual term.
    Type: Application
    Filed: April 2, 2020
    Publication date: October 15, 2020
    Inventors: James Allen Cox, Russell Albright, Saratendu Sethi
  • Patent number: 10458900
    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: Grant
    Filed: September 7, 2016
    Date of Patent: October 29, 2019
    Assignee: HONEYWELL INTERNATIONAL INC.
    Inventors: Terry Marta, James Allen Cox, Bernard Fritz, Antony Phillips, Rodney Watts
  • 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
  • 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: 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
  • 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
  • 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