Patents by Inventor Puneet Gajria

Puneet Gajria 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).

  • Anomaly detection using deep learning models
    Patent number: 11194691
    Abstract: A computer-implemented method for anomaly detection based on deep learning includes acquiring a plurality of records, each record having a corresponding number of attributes, identifying outliers in the plurality of records using labels generated from processing the plurality of records through an ensemble of different deep learning models, wherein an output of at least one model is used as an input to at least one other model and detecting anomalies in the plurality of records using a probabilistic classifier based on plurality of records and labels.
    Type: Grant
    Filed: May 31, 2019
    Date of Patent: December 7, 2021
    Assignee: GURUCUL SOLUTIONS, LLC
    Inventors: Nilesh Dherange, Saryu Nayyar, Naveen Vijayaraghavan, Puneet Gajria, Aruna Rajasekhar
  • Anomaly detection in cybersecurity and fraud applications
    Patent number: 11005872
    Abstract: A technique includes acquiring a plurality of records, each record having a corresponding number of attributes determining, based on local density measurements for numeric and normally distributed attribute value frequency measure for categorical attributes tags in the training portion of the plurality of records which is then used in probabilistic classifier for anomaly detection. A second set of implementations is proposed using ensemble method of combining deep learning algorithms for the same.
    Type: Grant
    Filed: May 31, 2019
    Date of Patent: May 11, 2021
    Assignee: GURUCUL SOLUTIONS, LLC
    Inventors: Nilesh Dherange, Saryu Nayyar, Naveen Vijayaraghavan, Puneet Gajria, Alexey Varganov
  • ANOMALY DETECTION USING DEEP LEARNING MODELS
    Publication number: 20200379868
    Abstract: A computer-implemented method for anomaly detection based on deep learning includes acquiring a plurality of records, each record having a corresponding number of attributes, identifying outliers in the plurality of records using labels generated from processing the plurality of records through an ensemble of different deep learning models, wherein an output of at least one model is used as an input to at least one other model and detecting anomalies in the plurality of records using a probabilistic classifier based on plurality of records and labels.
    Type: Application
    Filed: May 31, 2019
    Publication date: December 3, 2020
    Inventors: Nilesh Dherange, Saryu Nayyar, Naveen Vijayaraghavan, Puneet Gajria, Aruna Rajasekhar
  • ANOMALY DETECTION IN CYBERSECURITY AND FRAUD APPLICATIONS
    Publication number: 20200382536
    Abstract: A technique includes acquiring a plurality of records, each record having a corresponding number of attributes determining, based on local density measurements for numeric and normally distributed attribute value frequency measure for categorical attributes tags in the training portion of the plurality of records which is then used in probabilistic classifier for anomaly detection. A second set of implementations is proposed using ensemble method of combining deep learning algorithms for the same.
    Type: Application
    Filed: May 31, 2019
    Publication date: December 3, 2020
    Inventors: Nilesh Dherange, Saryu Nayyar, Naveen Vijayaraghavan, Puneet Gajria, Alexey Varganov