Patents by Inventor Ben Covington

Ben Covington 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: 11056220
    Abstract: An intensity transform augmentation system is operable to generate a plurality of sets of augmented images by performing a set of intensity transformation functions on each of a training set of medical scans. Each of the set of intensity transformation functions are based on density properties of corresponding anatomy feature present in the training set of medical scans. A computer vision model is generated by performing a training step on the plurality of sets of augmented images, where each augmented image of a set of augmented images is assigned same output label data based on a corresponding one of the training set of medical scans. Inference data is generated by performing an inference function on a new medical scan by utilizing the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.
    Type: Grant
    Filed: March 21, 2019
    Date of Patent: July 6, 2021
    Assignee: Enlitic, Inc.
    Inventors: Kevin Lyman, Li Yao, Eric C. Poblenz, Jordan Prosky, Ben Covington, Anthony Upton
  • Patent number: 11011257
    Abstract: A multi-label heat map display system is operable to receive a medical scan and a set of heat maps set of heat maps that each correspond to probability matrix data generated for each of a set of abnormality classes. An interactive interface that displays image data of the medical scan and at least one of the set of heat maps is generated for display on a display device associated with the multi-label heat map display system. User input to a client device is received, and an updated interactive interface that includes a change to the display of the at least one of the set of heat maps by the second portion of the interactive interface in response to the user input is displayed.
    Type: Grant
    Filed: March 12, 2019
    Date of Patent: May 18, 2021
    Assignee: Enlitic, Inc.
    Inventors: Lionel Lints, Li Yao, Kevin Lyman, Chris Croswhite, Ben Covington, Anthony Upton
  • Publication number: 20210118533
    Abstract: A triage routing system is operable to receive a medical scan via a receiver. Inference data for the medical scan is generated by performing an inference function, where the inference function utilizes a computer-vision model trained on a plurality of medical scans. One of a plurality of medical professionals is selected to review the medical scan based on the inference data. Triage routing data that indicates the medical scan and the one of the plurality of medical professionals is generated. The medical scan is transmitted to a client device associated with the one of the plurality of medical professionals for display via a display device in accordance with the triage routing data.
    Type: Application
    Filed: December 2, 2020
    Publication date: April 22, 2021
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Anthony Upton, Li Yao, Ben Covington
  • Publication number: 20210082547
    Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.
    Type: Application
    Filed: September 16, 2020
    Publication date: March 18, 2021
    Applicant: Enlitic, Inc.
    Inventors: Li Yao, Jordan Prosky, Eric C. Poblenz, Kevin Lyman, Lionel Lints, Ben Covington, Anthony Upton
  • Publication number: 20210082545
    Abstract: A medical evaluation system operates by: receiving a set of medical scans of a medical scan protocol captured for a patient, the set of medical scans corresponding to a proper subset of a plurality of sequence types; generating abnormality data by performing an inference function on the set of medical scans, wherein the inference function utilizes a computer vision model trained on a plurality of medical scans corresponding to the proper subset of the plurality of sequence types; calculating a confidence score for the abnormality data; generating first additional sequence data, wherein when the confidence score compares unfavorably to a confidence score threshold, the first additional sequence data indicates at least one first additional medical scan of the patient, corresponding to a first at least one of the plurality of sequence types not included in the proper subset of the plurality of sequence types, and when the confidence score compares favorably to the confidence score threshold, the first additional
    Type: Application
    Filed: November 19, 2020
    Publication date: March 18, 2021
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Anthony Upton, Ben Covington
  • Publication number: 20210074394
    Abstract: An intensity transform augmentation system is operable to receive a training set of medical scans. Random intensity transformation function parameters are generated for each medical scan of the training set of medical scans. A plurality of augmented images are generated, where each of the plurality of augmented images is generated by performing a intensity transformation function on one of the training set of medical scans by utilizing the random intensity transform parameters generated for the one of the training set of medical scan. A computer vision model is generated by performing a training step on the plurality of augmented images. A new medical scan is received via the receiver. Inference data is generated by performing an inference function that utilizes the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.
    Type: Application
    Filed: November 20, 2020
    Publication date: March 11, 2021
    Applicant: Enlitic, Inc.
    Inventors: Jordan Prosky, Li Yao, Eric C. Poblenz, Kevin Lyman, Ben Covington, Anthony Upton
  • Patent number: 10930387
    Abstract: A chest x-ray differential diagnosis system is operable to generate abnormality pattern data is generated for each of a received plurality of chest x-rays by identifying at least one pattern in each chest x-ray corresponding to an abnormality by utilizing a computer vision model that is trained on a plurality of training chest x-rays. Differential diagnosis data is generated for each chest x-ray based on the abnormality pattern data. Filtering parameters are received from a client device, and a filtered chest x-ray queue that includes a subset of chest x-rays is selected based on the filtering parameters and the differential diagnosis data is generated for transmission to the client device for display. Differential diagnosis data corresponding a chest x-ray indicated in chest x-ray selection data received from the client device is transmitted to the client device for display via the display device in conjunction with the chest x-ray.
    Type: Grant
    Filed: December 10, 2019
    Date of Patent: February 23, 2021
    Assignee: Enlitic, Inc.
    Inventors: Kevin Lyman, Devon Bernard, Li Yao, Diogo Almeida, Ben Covington, Anthony Upton
  • Patent number: 10902940
    Abstract: A triage routing system is operable to receive a medical scan via a receiver. Inference data for the medical scan is generated by performing an inference function, where the inference function utilizes a computer-vision model trained on a plurality of medical scans. One of a plurality of medical professionals is selected to review the medical scan based on the inference data. Triage routing data that indicates the medical scan and the one of the plurality of medical professionals is generated. The medical scan is transmitted to a client device associated with the one of the plurality of medical professionals for display via a display device in accordance with the triage routing data.
    Type: Grant
    Filed: March 18, 2019
    Date of Patent: January 26, 2021
    Assignee: Enlitic, Inc.
    Inventors: Kevin Lyman, Anthony Upton, Li Yao, Ben Covington
  • Patent number: 10896753
    Abstract: A lung screening assessment system is operable to receive a chest computed tomography (CT) scan that includes a plurality of cross sectional images. Nodule classification data of the chest CT scan is generated by utilizing a computer vision model that is trained on a plurality of training chest CT scans to identify a nodule in the plurality of cross sectional images and determine an assessment score. A lung screening report that includes the assessment score of the nodule classification data is generated for display on a display device associated with a user of the lung screening assessment system.
    Type: Grant
    Filed: December 10, 2019
    Date of Patent: January 19, 2021
    Assignee: Enlitic, Inc.
    Inventors: Kevin Lyman, Devon Bernard, Li Yao, Ben Covington, Diogo Almeida, Brian Basham, Jeremy Howard, Anthony Upton, John Zedlewski
  • Patent number: 10878949
    Abstract: An intensity transform augmentation system is operable to receive a training set of medical scans. Random intensity transformation function parameters are generated for each medical scan of the training set of medical scans. A plurality of augmented images are generated, where each of the plurality of augmented images is generated by performing a intensity transformation function on one of the training set of medical scans by utilizing the random intensity transform parameters generated for the one of the training set of medical scan. A computer vision model is generated by performing a training step on the plurality of augmented images. A new medical scan is received via the receiver. Inference data is generated by performing an inference function that utilizes the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.
    Type: Grant
    Filed: March 21, 2019
    Date of Patent: December 29, 2020
    Assignee: Enlitic, Inc.
    Inventors: Jordan Prosky, Li Yao, Eric C. Poblenz, Kevin Lyman, Ben Covington, Anthony Upton
  • Patent number: 10878948
    Abstract: A mid-protocol evaluation system is operable to receive a set of medical scans corresponding to a proper subset of a plurality of sequence types in a medical scan protocol captured for a patient. Abnormality data is generated by performing an inference function on the set of medical scans, where the inference function utilizes a computer vision model trained on a plurality of medical scans corresponding to the subset of the plurality of sequence types. A confidence score for the abnormality data is calculated, and additional sequence necessity data is generated for transmission to a client device for display via a display device. The additional sequence necessity data indicates at least one additional medical scan is necessary when the confidence score compares unfavorably to a confidence score threshold. The additional sequence necessity data indicates no further medical scans are necessary when the confidence score compares favorably to the confidence score threshold.
    Type: Grant
    Filed: March 18, 2019
    Date of Patent: December 29, 2020
    Assignee: Enlitic, Inc.
    Inventors: Kevin Lyman, Anthony Upton, Ben Covington
  • Publication number: 20200381093
    Abstract: A clinical trial re-evaluation system is operable to perform at least one assessment function on a set of medical scans for each of a first subset of a set of patients of a failed clinical trial to generate automated assessment data for each of the first subset of the set of patients. The first subset of the set of patients corresponds to a subset of human assessment data determined to have failed to meet criteria of the clinical trial. Patient re-evaluation data is generated for each of the first subset of the set of patients by comparing the automated assessment data to the criteria. The patient re-evaluation data for a second subset of the first subset of the set of patients indicates the automated assessment data passes the criteria. Trial re-evaluation data is generated based on the patient re-evaluation data for transmission to a computing device for display.
    Type: Application
    Filed: August 19, 2020
    Publication date: December 3, 2020
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Keith Lui, Anthony Upton, Li Yao, Ben Covington
  • Publication number: 20200373003
    Abstract: A medical scan triaging system is operable to generate a global abnormality probability for each of a plurality of medical scans by utilizing a computer vision model trained on a training set of medical scans; generate comparison data by comparing the global abnormality probability for each of the plurality of medical scans to a triage probability threshold; sort the plurality of medical scans, based on the comparison data, into a first subset of the plurality of medical scans, each having one of the plurality of abnormalities present, and a second subset of the plurality of medical scans, each having all of the plurality of abnormalities absent; and facilitate transmission, based on the sorting, of the first subset of the plurality of medical scans and the second subset of the plurality of medical scans to at least one medical scan viewing system.
    Type: Application
    Filed: August 12, 2020
    Publication date: November 26, 2020
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Li Yao, Eric C. Poblenz, Ben Covington, Anthony Upton
  • Publication number: 20200352518
    Abstract: A medical scan artifact detection system is operable to receive a medical scan of a patient. Artifact detection data is generated by executing an artifact detection function on the medical scan, where the artifact detection data indicates at least one artifact detected in the medical scan that includes a motion artifact or a nipple shadow. A notification is generated for display via a display device, where the notification indicates the at least one artifact.
    Type: Application
    Filed: July 27, 2020
    Publication date: November 12, 2020
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Ben Covington, Anthony Upton, David Di Domenico
  • Publication number: 20200357117
    Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of medical labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the medical labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Preliminary heat map visualization data can be generated for transmission to a client device based on the probability matrix data. Heat map visualization data can be generated via a post-processing of the preliminary heat map visualization data to mitigate heat map artifacts.
    Type: Application
    Filed: July 27, 2020
    Publication date: November 12, 2020
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Eric C. Poblenz, Li Yao, Ben Covington, Anthony Upton
  • Patent number: 10818386
    Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.
    Type: Grant
    Filed: March 12, 2019
    Date of Patent: October 27, 2020
    Assignee: Enlitic, Inc.
    Inventors: Li Yao, Jordan Prosky, Eric C. Poblenz, Kevin Lyman, Lionel Lints, Ben Covington, Anthony Upton
  • Patent number: 10783990
    Abstract: A clinical trial re-evaluation system is operable to perform at least one assessment function on a set of medical scans for each of a first subset of a set of patients of a failed clinical trial to generate automated assessment data for each of the first subset of the set of patients. The first subset of the set of patients corresponds to a subset of human assessment data determined to have failed to meet criteria of the clinical trial. Patient re-evaluation data is generated for each of the first subset of the set of patients by comparing the automated assessment data to the criteria. The patient re-evaluation data for a second subset of the first subset of the set of patients indicates the automated assessment data passes the criteria. Trial re-evaluation data is generated based on the patient re-evaluation data for transmission to a computing device for display.
    Type: Grant
    Filed: March 14, 2019
    Date of Patent: September 22, 2020
    Assignee: Enlitic, Inc.
    Inventors: Kevin Lyman, Keith Lui, Anthony Upton, Li Yao, Ben Covington
  • Patent number: 10755811
    Abstract: A medical scan comparison system is operable to receive a medical scan via a network and to generate similar scan data. The similar scan data includes a subset of medical scans from a medical scan database and is generated by performing an abnormality similarity function to determine that a set of abnormalities included in the subset of medical scans compare favorably to an abnormality identified in the medical scan. At least one cross-sectional image is selected from each medical scan of the subset of medical scans for display on a display device associated with a user of the medical scan comparison system in conjunction with the medical scan.
    Type: Grant
    Filed: May 23, 2019
    Date of Patent: August 25, 2020
    Assignee: Enlitic, Inc.
    Inventors: Devon Bernard, Kevin Lyman, Li Yao, Anthony Upton, Ben Covington, Jeremy Howard
  • Publication number: 20200160942
    Abstract: An automatic patient recruitment system is operable to determine a set of eligibility criteria, which includes abnormality criteria and other patient criteria, for each of a plurality of pharmaceutical studies. Abnormality data is generated for received medical scans by performing at least one inference function on image data of each medical scans by utilizing a computer vision model trained on a training set of medical scans. One of a plurality of patients is identified to be eligible for a pharmaceutical study by determining a medical scan of the patient has abnormality data that compares favorably to the abnormality criteria of the pharmaceutical study and by determining that the patient has patient data that compares favorably to the other patient criteria of the pharmaceutical study. A notification indicating the identified patient is eligible for the pharmaceutical study is generated for transmission to a client device.
    Type: Application
    Filed: May 10, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Anthony Upton, Li Yao, Ben Covington, Keith Lui
  • Publication number: 20200160978
    Abstract: An intensity transform augmentation system is operable to receive a training set of medical scans. Random intensity transformation function parameters are generated for each medical scan of the training set of medical scans. A plurality of augmented images are generated, where each of the plurality of augmented images is generated by performing a intensity transformation function on one of the training set of medical scans by utilizing the random intensity transform parameters generated for the one of the training set of medical scan. A computer vision model is generated by performing a training step on the plurality of augmented images. A new medical scan is received via the receiver. Inference data is generated by performing an inference function that utilizes the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.
    Type: Application
    Filed: March 21, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Jordan Prosky, Li Yao, Eric C. Poblenz, Kevin Lyman, Ben Covington, Anthony Upton