Abstract: An automated system and method for analyzing a set of extracted strings from a binary is disclosed including processing the binary with a string-extraction logic that can locate strings within the binary and output an extracted string set for use in cybersecurity analysis. The logic retrieves a set of training data comprising a plurality of previously analyzed extracted string sets where each element of the previously analyzed extracted string set comprises at least one extracted string and a corresponding previously determined threat prediction score. A prediction model based upon the training data is generated and the extracted string set is processed by the prediction model to determine a threat prediction score for each string. Ranking of the located strings is based upon the determined threat prediction score, and an output of a ranked string list is generated.

Abstract: An artificial intelligence (“AI”) engine having multiple independent processes on one or more computing platforms is disclosed, where the one or more computing platforms are located on premises of an organization such that i) the one or more computing platforms are configurable for one or more users in the organization having at least administrative rights on the one or more computing platforms in order to configure hardware components thereof to execute and load the multiple independent processes of the AI engine; ii) the one or more users of the organization are able to physically access the one or more computing platforms; and iii) the hardware components of the one or more computing platforms are connected to each other through a Local Area Network (LAN), and the LAN is configurable such that the one or more users in the organization have a right to control an operation of the LAN.

Abstract: The multiple independent processes run in an AI engine on its cloud-based platform. The multiple independent processes are configured as an independent process wrapped in its own container so that multiple instances of the same processes can run simultaneously to scale to handle one or more users to perform actions. The actions to solve AI problems can include 1) running multiple training sessions on two or more AI models at the same time, 2) creating two or more AI models at the same time, 3) running a training session on one or more AI models while creating one or more AI models at the same time, 4) deploying and using two or more trained AI models to do predictions on data from one or more data sources, 5) etc. A service handles scaling by dynamically calling in additional computing devices to load on and run additional instances of one or more of the independent processes as needed.

Abstract: An artificial intelligence (“AI”) engine having multiple independent processes on one or more computing platforms is disclosed, where the one or more computing platforms are located on premises of an organization such that i) the one or more computing platforms are configurable for one or more users in the organization having at least administrative rights on the one or more computing platforms in order to configure hardware components thereof to execute and load the multiple independent processes of the AI engine; ii) the one or more users of the organization are able to physically access the one or more computing platforms; and iii) the hardware components of the one or more computing platforms are connected to each other through a Local Area Network (LAN), and the LAN is configurable such that the one or more users in the organization have a right to control an operation of the LAN.

Abstract: A system for translating user motion into multiple object responses of an on-screen object based on user interaction of an application executing on a computing device is provided. User motion data is received from a capture device from one or more users. The user motion data corresponds to user interaction with an on-screen object presented in the application. The on-screen object corresponds to an object other than an on-screen representation of a user that is displayed by the computing device. The user motion data is automatically translated into multiple object responses of the on-screen object. The multiple object responses of the on-screen object are simultaneously displayed to the users.

Abstract: A system for translating user motion into multiple object responses of an on-screen object based on user interaction of an application executing on a computing device is provided. User motion data is received from a capture device from one or more users. The user motion data corresponds to user interaction with an on-screen object presented in the application. The on-screen object corresponds to an object other than an on-screen representation of a user that is displayed by the computing device. The user motion data is automatically translated into multiple object responses of the on-screen object. The multiple object responses of the on-screen object are simultaneously displayed to the users.

Abstract: A fuel rail is defined by a spacer member sandwiched between a throttle body and an engine intake manifold, the spacer member having air channels connecting the throttle body air induction passages to the manifold inlet bores, the spacer member also having at least one longitudinal bore constituting a fuel passage, and fuel injector receiving cups communicating with the fuel passages.