Dynamically-updatable deep transactional monitoring systems and methods

    公开(公告)号:US12026523B1

    公开(公告)日:2024-07-02

    申请号:US18464162

    申请日:2023-09-08

    Inventor: Beth Hunt

    CPC classification number: G06F9/44521 G06F11/302 G06F11/3495

    Abstract: Provided herein are system, method and computer program products for providing dynamically-updatable deep transactional monitoring of running applications in real-time. A method for monitoring a target software application operates by injecting a software engine into a new thread within a target process of the target software application. The method then retrieves a monitoring script and initiates execution of the monitoring script within the software engine. The monitoring script determining the address functions and calls to the functions and inserts a trampoline call within the one or more functions. The trampoline saves the execution state of the target process and calls a corresponding monitoring function that to retrieves data associated with the target process. The method then restoring the execution state of the target process and resumes execution of the target function.

    Object feature visualization apparatus and methods

    公开(公告)号:US12020353B2

    公开(公告)日:2024-06-25

    申请号:US18113394

    申请日:2023-02-23

    Inventor: Adam I. Sandow

    Abstract: An object feature visualization system is disclosed. The system may include a computing device that generates video-mapped images to project onto physical objects. The video-mapped images may include features to be projected onto the objects. The projection of a video-mapped image onto the physical object allows for the visualization of the feature on the object. In some examples, the computing device receives a feature selection for a particular object, and generates a video-mapped image with the selected feature to provide to a projector to project the video-mapped image onto the physical object. In some examples, a user is able to select one or more features for one or more objects of a room display via a user interface. The system then projects video-mapped images with the selected features onto the physical objects. The system may allow a user to save feature selections, and to purchase or request additional information about objects with selected features.

    DIGITAL SIGN NETWORK
    57.
    发明公开

    公开(公告)号:US20240192823A1

    公开(公告)日:2024-06-13

    申请号:US18540689

    申请日:2023-12-14

    Abstract: The disclosed subject matter provides a computing system configured to present a map-centric interface to a user, the map-centric interface usable by the user to control selected ones of a plurality of digital signs. The computing system is further configured to receive via a network interface travel data indicative of availability of travel resources, data corresponding to emergency events, and instructions from a third-party entity regarding the emergency events. The computing system is further configured to connect to a particular digital sign of the selected ones and display information corresponding to the received travel data associated with a geographic region in which the digital sign is located. In addition, the computing system is configured to display on one or more of the plurality of digital signs emergency event messages based on the data corresponding to emergency events.

    Information processing apparatus and information processing method

    公开(公告)号:US12002488B2

    公开(公告)日:2024-06-04

    申请号:US17548743

    申请日:2021-12-13

    Inventor: Aqmar Muhammad

    Abstract: Disclosed herein is an information processing apparatus, comprising: a feature extraction unit configured to extract features from a sample of a first class and a sample of a second class contained in a source domain and a sample of the first class contained in a target domain, respectively; a pseudo-sample generation unit configured to generate pseudo-samples of the second class in the target domain based on a distribution of samples of the first class contained in the target domain in a feature space of the features extracted by the feature extraction unit; and a data transformation unit configured to perform data transformation in the feature space by machine learning such that a distribution of samples of the first class and samples of the second class contained in the source domain approximates a distribution of samples of the first class and the pseudo-samples of the second class in the target domain.

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