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    METHODS AND SYSTEMS TO APPLY DIGITAL INTERVENTIONS BASED ON MACHINE LEARNING MODEL OUTPUT
    1.
    发明申请
    METHODS AND SYSTEMS TO APPLY DIGITAL INTERVENTIONS BASED ON MACHINE LEARNING MODEL OUTPUT 审中-公开

    公开(公告)号：WO2022178458A1

    公开(公告)日：2022-08-25

    申请号：PCT/US2022/017393

    申请日：2022-02-22

    Applicant: DEEP LABS, INC.

    Inventor： HARRIS, Theodore ,  NOVAK, Jiri ,  EDINGTON, Scott ,  NILSSON, Simon ,  BECK, Talia

    IPC: G06E1/00

    Abstract: A system and a method for providing a digital intervention relating to user interactions. A system may have at least one processor configured to perform operations comprising: receiving input data from at least one client device; accessing a data model configured to determine, based on historical data, risk levels associated with the user interactions; inserting the input data into the data model; receiving, from the data model, an indication that at least one determined risk level associated with the user interactions exceeds a preset threshold; and providing, in response to the at least one determined risk level exceeding the preset threshold, the digital intervention.

    SYSTEMS AND METHODS FOR GENERATING HASH TREES AND USING NEURAL NETWORKS TO PROCESS THE SAME
    2.
    发明申请
    SYSTEMS AND METHODS FOR GENERATING HASH TREES AND USING NEURAL NETWORKS TO PROCESS THE SAME 审中-公开

    公开(公告)号：WO2019222761A1

    公开(公告)日：2019-11-21

    申请号：PCT/US2019/033184

    申请日：2019-05-20

    Applicant: DEEP LABS INC.

    Inventor： FAITH, Patrick ,  EDINGTON, Scott

    IPC: G06N3/02 ,  G06N3/063 ,  G06N3/08

    Abstract: The present disclosure relates to systems and methods for detecting and detecting anomalies within a tree structure. In one implementation, the system may include one or more memories storing instructions and one or more processors configured to execute the instructions. The instructions may include instructions to receive a new data structure related to an individual, convert the data structure into a Bayesian wavelet, using a tree structure of existing Bayesian wavelets, calculate one or more harmonics, determine a measure of whether the Bayesian wavelet alters the one or more harmonics, and add the Bayesian wavelet to the tree structure when the measure is below a threshold.

    INTELLIGENT FRICTION FOR AUTHENTICATION METHODS AND SYSTEMS
    3.
    发明申请
    INTELLIGENT FRICTION FOR AUTHENTICATION METHODS AND SYSTEMS 审中-公开

    公开(公告)号：WO2022178436A2

    公开(公告)日：2022-08-25

    申请号：PCT/US2022/017346

    申请日：2022-02-22

    Applicant: DEEP LABS, INC.

    Inventor： HARRIS, Theodore ,  NOVAK, Jiri ,  EDINGTON, Scott

    IPC: G06E1/00 ,  G06F21/316 ,  G06F21/554 ,  G06F3/0481 ,  H04L63/08 ,  H04L63/1491

    Abstract: A system and a method for providing intelligent friction by receiving user information based on an interaction of a user with a user interface; and providing intelligent friction through the user interface using an intelligent system. This may comprise changing the user interface relative to a baseline based on the user information. Intelligent friction may provide changes to the user interface. The system may increase security and prevent unauthorized access by bad actors. The system may be less susceptible to hacking by altering how verification or authentication is performed. Systems using intelligent friction may be easily implemented because they may be less reliant on user devices. Rather than requiring multiple communication channels as may be the case in multi-factor authentication, intelligent friction may be advantageously carried out with the user using mobile devices of low complexity.

    SYSTEMS AND METHODS FOR ADAPTIVE TRAINING NEURAL NETWORKS
    4.
    发明申请
    SYSTEMS AND METHODS FOR ADAPTIVE TRAINING NEURAL NETWORKS 审中-公开

    公开(公告)号：WO2022245949A1

    公开(公告)日：2022-11-24

    申请号：PCT/US2022/029840

    申请日：2022-05-18

    Applicant: DEEP LABS, INC.

    Inventor： FAITH, Patrick ,  EDINGTON, Scott

    IPC: G06N3/08 ,  G06K9/62

    Abstract: The present disclosure relates to systems and methods for creating and training neural networks. The method includes collecting a set of signals from a database; applying a transform to each signal to create a modified set of signals, wherein signals of the modified set of signals are wavelets; iteratively, for each of a subset of the modified signals: training the neural network using a modified signal of the subset by adding at least one node to the neural network in response to an error function of an analysis of the modified signal exceeding a threshold; removing nodes from the neural network with activation rates below an activation rate threshold; and grouping each node into a lobe among a plurality of lobes, wherein nodes belonging to a lobe have a common characteristic.

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