Opswat, Inc.

8Flow Inc.

Frank Dye

Benjamin Czarny

Yiyi Miao

Shae Anthony Bettencourt

Yev Goldin

Josh Russ

Bill Zhao

Boaz Hecht

In an embodiment, a workflow automation computer system comprises one or more hardware processors; one or more network interfaces that are communicatively coupled to one or more internetworks and capable of network communication with a browser extension hosted on an agent computer, a relational database system, and a support ticket system; and one or more non-transitory computer-readable storage media coupled to the one or more hardware processors and storing one or more trained machine learning models having been trained to output predictions of actions of web-based applications based on input specifying a plurality of browser events from interactions with the web-based applications; and one or more sequences of instructions which, when executed using one or more processors, cause the one or more processors to execute receiving, from the browser extension, one or more browser event objects corresponding to user input signals arising from interactions of the agent computer with the web-based applications, extracting attribute values from the one or more browser event objects, and storing, in the relational database system, the one or more browser event objects and attribute values as a plurality of events of user action time series records; clustering the plurality of events and performing action analysis on the plurality of events by executing a first inference stage of the one or more trained machine learning models over the one or more events to output suggestions of associations of user actions corresponding to the one or more events; forming one or more training data records based on the associations of user actions corresponding to the one or more events; re-training the one or more trained machine learning models based on the associations of user actions to produce one or more re-trained machine learning models.

Machine learning automation of network resource workflows

A system receives binary data and first identification data. The binary data includes hashes of strings of bits, bytes, words or characters. The system receives vulnerability data and second identification data. The system determines a correspondence between the binary data and the vulnerability data based on matching the first identification data with the second identification data. The vulnerability data includes a country of origin for a product identified by the second identification data. The system generates a binaries-to-vulnerabilities database. The system scans target binary data from a target device to to find matches between the target binary data and the binary data using the binaries-to-vulnerabilities database. The system determines a known security vulnerability based on the results of the scanning and the correspondence between the binary data and the vulnerability data. The known security vulnerability includes the country of origin for the product in the target device.

Computer security vulnerability assessment

Growing community of inventors

San Francisco, CA, United States of America

Frank Dye