TRANSFORMATION AS A SERVICE
    2.
    发明公开

    公开(公告)号:US20230143392A1

    公开(公告)日:2023-05-11

    申请号:US18149787

    申请日:2023-01-04

    CPC classification number: G06F16/168 G06F16/256 G06F16/258

    Abstract: Aspects of the subject disclosure may include, for example, receiving input data via a transformation UI, generating transformation configuration data, causing the transformation UI to present transformation object data per the transformation configuration data, where the transformation object data identifies data objects each including an input and output field name and a data type, detecting, from the transformation UI, an instruction defining a mapping for the input data, including a modification to the output field name of a data object such that the input field name of the data object is mapped to the modified output field name, based on the detecting the instruction, modifying the first transformation configuration data per the mapping to derive second transformation configuration data, performing a transformation of the input data based on the second transformation configuration data, and causing the transformation UI to present a transformation output. Other embodiments are disclosed.

    DATA HARMONIZATION ACROSS MULTIPLE SOURCES

    公开(公告)号:US20250021541A1

    公开(公告)日:2025-01-16

    申请号:US18902429

    申请日:2024-09-30

    Abstract: In another example, a device includes a processor and a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations. The operations include acquiring a plurality of data items from a plurality of data sources, wherein the at least two data sources data sources of the plurality of data sources are maintained by different entities, normalizing attributes of the plurality of data items, using a first machine learning technique, matching at least two data items of the plurality of data items to form a grouping, wherein the matching is based on similarities observed in the attributes of the at least two data items subsequent to the normalizing, and creating a single profile for an individual associated with the at least two data items, based on the grouping, wherein the single profile consolidates the attributes of the at least two data items.

    RESTRICTED REUSE OF MACHINE LEARNING MODEL DATA FEATURES

    公开(公告)号:US20240095579A1

    公开(公告)日:2024-03-21

    申请号:US17949787

    申请日:2022-09-21

    CPC classification number: G06N20/00

    Abstract: A processing system including at least one processor may obtain a request from a first entity to train a machine learning model, access at least one data feature of at least a second entity, and train the machine learning model on behalf of the first entity in accordance with the at least one data feature of the at least the second entity to generate a trained machine learning model, where the at least one data feature of the at least the second entity is a restricted data feature that is inaccessible to the first entity. The processing system may then provide the trained machine learning model to the first entity.

    CALL GRAPHS FOR TELECOMMUNICATION NETWORK ACTIVITY DETECTION

    公开(公告)号:US20230216968A1

    公开(公告)日:2023-07-06

    申请号:US17566886

    申请日:2021-12-31

    CPC classification number: H04M15/58 G06N5/022 H04M15/47

    Abstract: A processing system may maintain a communication graph that includes nodes representing a plurality of phone numbers including a first phone number and edges between the nodes representing a plurality of communications between the plurality of phone numbers and may generate at least one vector via a graph embedding process applied to the communication graph, the at least one vector representing features of at least a portion of the communication graph. The processing system may then apply the at least one vector to a prediction model that is implemented by the processing system and that is configured to predict whether the first phone number is associated with a type of network activity associated with a telecommunication network and may implement a remedial action in response to an output of the prediction model indicating that the first phone number is associated with the type of network activity.

    DETECTING THREAT PATHWAYS USING SEQUENCE GRAPHS

    公开(公告)号:US20220394049A1

    公开(公告)日:2022-12-08

    申请号:US17338646

    申请日:2021-06-03

    Abstract: A method for detecting threat pathways using sequence graphs includes constructing a sequence graph from a set of data containing information about activities in a telecommunications service provider network, where the sequence graph represents a subset of the activities that occurs as a sequence, providing an embedding of the sequence graph as input to a machine learning model, wherein the machine learning model has been trained to detect when an input embedding of a sequence graph is likely to indicate a threat activity, determining, based on an output of the machine learning model, whether the subset of the activities is indicative of the threat activity, and initiating a remedial action to mitigate the threat activity.

    DATA STREAM BASED EVENT SEQUENCE ANOMALY DETECTION FOR MOBILITY CUSTOMER FRAUD ANALYSIS

    公开(公告)号:US20220366430A1

    公开(公告)日:2022-11-17

    申请号:US17321279

    申请日:2021-05-14

    Abstract: Data stream based event sequence anomaly detection for mobility customer fraud analysis is presented herein. A system obtains a sequence of events comprising respective modalities of communication that correspond to a subscriber identity associated with a communication service—the sequence of events having occurred within a defined period. Based on defined classifiers representing respective fraudulent sequences of events, the system determines, via a group of machine learning models corresponding to respective machine learning processes, whether the sequence of events satisfies a defined condition with respect to likelihood of representing a fraudulent sequence of events of the respective fraudulent sequences of events. In response to the sequence of events being determined to satisfy the defined condition, the system sends, via a user interface of the system, a notification indicating that the sequence of events has been determined to represent the fraudulent sequence of events.

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