Patents by Inventor Anton Duppils

Anton Duppils 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: 12265612
    Abstract: Open-source software is prevalent in the development of new technologies. Monitoring software updates for vulnerabilities is expensive and time consuming. Online discussions surrounding new software updates can often provide vital information regarding emerging risks. It is presented a novel approach for automating surveillance of software through the use of natural language processing methods on open-source issues. Further, the potential of virtual adversarial training, a popular semi-supervised learning technique, is used to leverage the vast amounts of unlabeled data available to achieve improved performance. On industry data, it is found that a hierarchical attention network with virtual adversarial training that utilizes the innate document structure to encapsulate the text can be used with good results.
    Type: Grant
    Filed: January 22, 2021
    Date of Patent: April 1, 2025
    Assignee: DEBRICKED AB
    Inventors: Anton Duppils, Magnus Tullberg, Carl Emil Orm Wåreus
  • Publication number: 20240241963
    Abstract: Embodiments of the disclosure provide systems and methods for accurately identifying functions in software code that represent vulnerabilities. Identifying vulnerable functions in software code can comprise collecting information identifying one or more known Common Vulnerabilities and Exposures (CVEs) and identifying one or more vulnerable functions in the software code based on relationships between the collected information identifying the one or more known CVEs and the one or more vulnerable functions in the software code. A call graph can be derived for the software code based on the identified one or more vulnerable functions. Each of the identified one or more vulnerable functions can be indicated in the call graph by a vulnerability symbol. A determination can be made as to whether each identified one or more vulnerable functions is a true vulnerability, i.e., when the vulnerable function is encountered when traversing the call graph.
    Type: Application
    Filed: January 18, 2023
    Publication date: July 18, 2024
    Applicant: MICRO FOCUS LLC
    Inventors: Emil Wareus, Magnus Tullberg, Anton Duppils
  • Publication number: 20230036159
    Abstract: Open-source software is prevalent in the development of new technologies. Monitoring software updates for vulnerabilities is expensive and time consuming. Online discussions surrounding new software updates can often provide vital information regarding emerging risks. It is presented a novel approach for automating surveillance of software through the use of natural language processing methods on open-source issues. Further, the potential of virtual adversarial training, a popular semi-supervised learning technique, is used to leverage the vast amounts of unlabeled data available to achieve improved performance. On industry data, it is found that a hierarchical attention network with virtual adversarial training that utilizes the innate document structure to encapsulate the text can be used with good results.
    Type: Application
    Filed: January 22, 2021
    Publication date: February 2, 2023
    Applicant: debricked AB
    Inventors: Anton Duppils, Magnus Tullberg, Emil Wåreus