Patents by Inventor Matthew Kindy, II

Matthew Kindy, II 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).

  • SYSTEM AND METHOD FOR AUTOMATICALLY DETECTING A SECURITY VULNERABILITY IN A SOURCE CODE USING A MACHINE LEARNING MODEL
    Publication number: 20230177170
    Abstract: There is disclosed a method of automatically detecting a security vulnerability in a source code using a machine learning model, characterized in that the method comprises: obtaining the source code from a client codebase, wherein the client codebase is a complete or an incomplete body of the source code for a given software program or an application; and using a machine learning (ML) model to perform a ML based analysis on an abstract syntax tree (AST) for detecting a first security vulnerability over a static source code, the machine learning based analysis comprise (i) flattening the abstract syntax tree (AST) into a sequence of structured tokens, wherein the sequence of structured tokens comprises a semantic structure and a syntactic structure of the source code, (ii) implementing a natural language processing technique on the sequence of structured tokens for mapping the sequence of structured tokens to one or more integers, (iii) pre-training the machine learning model using an unlabeled source code as
    Type: Application
    Filed: January 30, 2023
    Publication date: June 8, 2023
    Applicant: PRAETORIAN
    Inventors: Jeff Olson, Matthew Kindy, II
  • System and method for automatically detecting a security vulnerability in a source code using a machine learning model
    Patent number: 11568055
    Abstract: A method for (of) automatically detecting a security vulnerability in a source code using a machine learning model, characterized in that the method includes: obtaining the source code from a client codebase, wherein the client codebase is a complete or an incomplete body of the source code for a given software program or an application; and using a machine learning (ML) model to perform a ML based analysis on an abstract syntax tree (AST) for detecting a first security vulnerability over a static source code, the machine learning based analysis comprise (i) flattening the abstract syntax tree (AST) into a sequence of structured tokens, wherein the sequence of structured tokens includes a semantic structure and a syntactic structure of the source code, (ii) implementing a natural language processing technique on the sequence of structured tokens for mapping the sequence of structured tokens to one or more integers, (iii) pre-training the machine learning model using an unlabeled source code as an input to pre
    Type: Grant
    Filed: August 23, 2019
    Date of Patent: January 31, 2023
    Assignee: Praetorian
    Inventors: Jeff Olson, Matthew Kindy, II
  • SYSTEM AND METHOD FOR AUTOMATICALLY DETECTING A SECURITY VULNERABILITY IN A SOURCE CODE USING A MACHINE LEARNING MODEL
    Publication number: 20210056211
    Abstract: A method for (of) automatically detecting a security vulnerability in a source code using a machine learning model, characterized in that the method includes: obtaining the source code from a client codebase, wherein the client codebase is a complete or an incomplete body of the source code for a given software program or an application; and using a machine learning (ML) model to perform a ML based analysis on an abstract syntax tree (AST) for detecting a first security vulnerability over a static source code, the machine learning based analysis comprise (i) flattening the abstract syntax tree (AST) into a sequence of structured tokens, wherein the sequence of structured tokens includes a semantic structure and a syntactic structure of the source code, (ii) implementing a natural language processing technique on the sequence of structured tokens for mapping the sequence of structured tokens to one or more integers, (iii) pre-training the machine learning model using an unlabeled source code as an input to pre
    Type: Application
    Filed: August 23, 2019
    Publication date: February 25, 2021
    Inventors: Jeff Olson, Matthew Kindy, II