公开(公告)号：US12231909B2

公开(公告)日：2025-02-18

申请号：US17731293

申请日：2022-04-28

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

Inventor： Vladimir Sevastyanov ,  Abhay Dabholkar ,  James Pratt ,  Rakhi Gupta ,  Nikhlesh Agrawal

IPC: H04W16/18 ,  H04W4/021 ,  H04W40/20

Abstract: Improving configuration of routing areas within an area of interest can include obtaining input data including a list of common language location identifiers within a defined area of interest and identification of a number of routing areas to be created within the area of interest; creating a bounding rectangle around the area of interest represented by the list of common language location identifiers; generating, within the bounding rectangle, a uniformly distributed sequence point; determining, from within the list of common language location identifiers, if the uniformly distributed sequence point is located in a particular common language location identifier of the list of common language location identifiers; if so, adding, to a list of kernel common language location identifiers the uniformly distributed sequence point; and if not, discarding the uniformly distributed sequence point.

公开(公告)号：US20230354051A1

公开(公告)日：2023-11-02

申请号：US17731293

申请日：2022-04-28

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

Inventor： Vladimir Sevastyanov ,  Abhay Dabholkar ,  James Pratt ,  Rakhi Gupta ,  Nikhlesh Agrawal

IPC: H04W16/18 ,  H04W40/20 ,  H04W4/021

CPC classification number: H04W16/18 ,  H04W40/205 ,  H04W4/021

Abstract: Improving configuration of routing areas within an area of interest can include obtaining input data including a list of common language location identifiers within a defined area of interest and identification of a number of routing areas to be created within the area of interest; creating a bounding rectangle around the area of interest represented by the list of common language location identifiers; generating, within the bounding rectangle, a uniformly distributed sequence point; determining, from within the list of common language location identifiers, if the uniformly distributed sequence point is located in a particular common language location identifier of the list of common language location identifiers; if so, adding, to a list of kernel common language location identifiers the uniformly distributed sequence point; and if not, discarding the uniformly distributed sequence point.

公开(公告)号：US20240004960A1

公开(公告)日：2024-01-04

申请号：US17856988

申请日：2022-07-02

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

Inventor： Vladimir Sevastyanov ,  James Pratt ,  Nikhlesh Agrawal ,  Abhay Dabholkar ,  Rakhi Gupta

IPC: G06K9/62 ,  G06K9/00

CPC classification number: G06K9/6261 ,  G06K9/00523 ,  H04W24/02

Abstract: A processing system including at least one processor may obtain a data set comprising a plurality of records, each record associating at least one feature value of at least one feature with a value of a target variable. The processing system may next segregate the plurality of records into a plurality of subsets based upon a range of values of the at least one feature and calculate a plurality of sub-volumes for the plurality of subsets, each sub-volume comprising a sum of the values of the target variable from records in a respective subset. The processing system may then generate a significance metric that is based on a difference between a highest sub-volume and a lowest sub-volume of the plurality of sub-volumes and select the at least one feature to train a classification model associated with the target variable, based upon the significance metric.

公开(公告)号：US20230359781A1

公开(公告)日：2023-11-09

申请号：US17736613

申请日：2022-05-04

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

Inventor： Vladimir Sevastyanov ,  Abhay Dabholkar ,  Rakhi Gupta ,  James H. Pratt ,  Nikhlesh Agrawal

IPC: G06F30/20

CPC classification number: G06F30/20 ,  G06F2111/10

Abstract: Aspects of the subject disclosure may include, for example, dividing a feature range of a feature into a plurality of subsets that span the feature range, calculating an average target variable value for each subset of the plurality of subsets, resulting in a plurality of average target variable values, and estimating a measure of feature significance with respect to a target variable by determining a difference between a maximum average target variable value and a minimum average target variable value in the plurality of average target variable values. Other embodiments are disclosed.