Patents by Inventor Liz Kao

Liz Kao 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).

  • Systems and methods for machine learning-based digital content clustering, digital content threat detection, and digital content threat remediation in machine learning-based digital threat mitigation platform
    Patent number: 11528290
    Abstract: A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse/digital fraud and not digital abuse/digital fraud; and identifying members or content clusters having digital fraud/digital abuse based on querying the distinct clusters of content data.
    Type: Grant
    Filed: April 6, 2022
    Date of Patent: December 13, 2022
    Assignee: Sift Science, Inc.
    Inventors: Wei Liu, Jintae Kim, Michael Legore, Yong Fu, Cat Perry, Rachel Mitrano, James Volz, Liz Kao
  • SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED DIGITAL CONTENT CLUSTERING, DIGITAL CONTENT THREAT DETECTION, AND DIGITAL CONTENT THREAT REMEDIATION IN MACHINE LEARNING-BASED DIGITAL THREAT MITIGATION PLATFORM
    Publication number: 20220232029
    Abstract: A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse/digital fraud and not digital abuse/digital fraud; and identifying members or content clusters having digital fraud/digital abuse based on querying the distinct clusters of content data.
    Type: Application
    Filed: April 6, 2022
    Publication date: July 21, 2022
    Inventors: Wei Liu, Jintae Kim, Michael Legore, Yong Fu, Cat Perry, Rachel Mitrano, James Volz, Liz Kao
  • Systems and methods for machine learning-based digital content clustering, digital content threat detection, and digital content threat remediation in machine learning task-oriented digital threat mitigation platform
    Patent number: 11330009
    Abstract: A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse/digital fraud and not digital abuse/digital fraud; and identifying members or content clusters having digital fraud/digital abuse based on querying the distinct clusters of content data.
    Type: Grant
    Filed: February 19, 2021
    Date of Patent: May 10, 2022
    Assignee: Sift Science, Inc.
    Inventors: Wei Liu, Jintae Kim, Michael Legore, Yong Fu, Cat Perry, Rachel Mitrano, James Volz, Liz Kao
  • SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED DIGITAL CONTENT CLUSTERING, DIGITAL CONTENT THREAT DETECTION, AND DIGITAL CONTENT THREAT REMEDIATION IN MACHINE LEARNING TASK-ORIENTED DIGITAL THREAT MITIGATION PLATFORM
    Publication number: 20210281593
    Abstract: A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse/digital fraud and not digital abuse/digital fraud; and identifying members or content clusters having digital fraud/digital abuse based on querying the distinct clusters of content data.
    Type: Application
    Filed: February 19, 2021
    Publication date: September 9, 2021
    Inventors: Wei Liu, Jintae Kim, Michael Legore, Yong Fu, Cat Perry, Rachel Mitrano, James Volz, Liz Kao