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    DATA HARMONIZATION ACROSS MULTIPLE SOURCES
    2.
    发明申请
    DATA HARMONIZATION ACROSS MULTIPLE SOURCES 有权

    公开(公告)号：US20250021541A1

    公开(公告)日：2025-01-16

    申请号：US18902429

    申请日：2024-09-30

    Applicant: AT&T Intellectual Property I, L.P.

    Inventor： Prince Paulraj ,  Shilpi Harpavat ,  Weiping Liu ,  Shreyash Taywade ,  Arjun Coimbatore Nagarasan ,  Yukun Zeng ,  Prabhu Gururaj

    IPC: G06F16/22 ,  G06N5/04 ,  G06N20/00 ,  G06Q30/016 ,  G06Q30/0201 ,  G06Q40/02

    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.

    Data harmonization across multiple sources
    3.
    发明授权
    Data harmonization across multiple sources 有权

    公开(公告)号：US11625379B2

    公开(公告)日：2023-04-11

    申请号：US16782754

    申请日：2020-02-05

    Applicant: AT&T Intellectual Property I, L.P.

    Inventor： Prince Paulraj ,  Shilpi Harpavat ,  Weiping Liu ,  Shreyash Taywade ,  Arjun Coimbatore Nagarasan ,  Yukun Zeng ,  Prabhu Gururaj

    IPC: G06F7/00 ,  G06F16/22 ,  G06N20/00 ,  G06Q40/02 ,  G06Q30/0201 ,  G06Q30/016 ,  G06N5/04

    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.

    STEERING OF ROAMING OPTIMIZATION WITH SUBSCRIBER BEHAVIOR PREDICTION
    4.
    发明申请
    STEERING OF ROAMING OPTIMIZATION WITH SUBSCRIBER BEHAVIOR PREDICTION 有权

    公开(公告)号：US20220383151A1

    公开(公告)日：2022-12-01

    申请号：US17331225

    申请日：2021-05-26

    Applicant: AT&T Intellectual Property I, L.P. ,  AT&T Mobility II LLC

    Inventor： Yukun Zeng ,  Prince Paulraj ,  Sherri Rogers ,  Scott Alexander ,  Shilpi Harpavat ,  Shreyash Taywade ,  Aubryn Lewis ,  Cameron Dunn ,  Sandeep David Golkonda ,  Roli Arora

    IPC: G06N5/04 ,  H04W4/24 ,  H04W24/08 ,  G06N20/00

    Abstract: Aspects of the subject disclosure may include, for example, obtaining roaming agreement data related to roaming agreements that are between a wireless provider and a respective one of a plurality of wireless roaming providers; obtaining, for each wireless subscriber of the wireless provider, respective roaming usage data, all of the respective roaming usage data comprising collective roaming usage data; training, based upon the collective roaming usage data, a set of one or more models, the one or more models comprising one or more statistical models, one or more machine learning models, or any combination thereof, the one or more models being trained with multiple iterations of feedback loops, and the training resulting in one or more trained models; estimating for each wireless subscriber, based upon the one or more trained models, respective projected location information for a future time, all of the respective projected location information comprising collective projected location information; obtaining, for each of a plurality of wireless coverage areas of the plurality of wireless roaming providers, respective real-time network quality measurement data, all of the respective real-time network quality measurement data comprising collective real-time network quality measurement data; modeling a plurality of scenarios for the future time based upon the roaming agreement data, based upon the collective real-time network quality measurement data and based upon the collective projected location information, each of the scenarios identifying for each of a plurality of projected future wireless roaming subscribers a respective one of the wireless roaming providers to communicate with at the future time, each of the scenarios further identifying a respective cost to the wireless provider, and the modeling being performed via use of a plurality of model constraints; selecting from the scenarios, as a selected scenario, a scenario that has associated therewith a lowest total cost to the wireless provider also satisfying one or more of the plurality of model constraints based upon the collective roaming agreement data; and sending recommendations, to a plurality of steering mechanisms, in order to implement the selected scenario. Other embodiments are disclosed.

    DATA HARMONIZATION ACROSS MULTIPLE SOURCES
    5.
    发明申请
    DATA HARMONIZATION ACROSS MULTIPLE SOURCES 有权

    公开(公告)号：US20210173822A1

    公开(公告)日：2021-06-10

    申请号：US16782754

    申请日：2020-02-05

    Applicant: AT&T Intellectual Property I, L.P.

    Inventor： Prince Paulraj ,  Shilpi Harpavat ,  Weiping Liu ,  Shreyash Taywade ,  Arjun Coimbatore Nagarasan ,  Yukun Zeng ,  Prabhu Gururaj

    IPC: G06F16/22 ,  G06N20/00 ,  G06N5/04 ,  G06Q30/02 ,  G06Q30/00 ,  G06Q40/02

    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.

    CODE-TO-UTILIZATION METRIC BASED CODE ARCHITECTURE ADAPTATION
    7.
    发明公开
    CODE-TO-UTILIZATION METRIC BASED CODE ARCHITECTURE ADAPTATION 审中-公开

    公开(公告)号：US20230142895A1

    公开(公告)日：2023-05-11

    申请号：US17520144

    申请日：2021-11-05

    Applicant: AT&T Intellectual Property I, L.P.

    Inventor： Andrew Campbell ,  Shreyash Taywade ,  Prince Paulraj

    IPC: G06F9/50 ,  G06F11/34 ,  G06F11/07

    CPC classification number: G06F9/505 ,  G06F11/3433 ,  G06F11/0709

    Abstract: Determining a recommendation to convert a block of code into a serverless function based on analysis of code in execution in a cloud computing environment is disclosed. The block of code can be correlated to high levels of computing resource utilization that can inflate a cost of deploying a corresponding application in the cloud computing environment by prophylactically increasing an amount of provisioned computing resources to accommodate the high-utilization of the block of code. Converting the block of code into a serverless function can reduce the cost via offloading the functionality from the code into a function call supported by the cloud computing environment in an as-needed capacity, thereby reducing the amount of prophylactically provisioned computing resources. The recommendation can occur continuously in a production environment and cot-to-utilization information can render to facilitate identification of code block conversion targets.

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