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公开(公告)号：US20210029182A1
公开(公告)日：2021-01-28
申请号：US16518229
申请日：2019-07-22
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Rudolph Mappus , Vijayan Nagarajan , Sheldon Meredith
Abstract: A processing system including at least one processor may collect a first set of time series features relating to requests for a content item at a content distribution node in a communication network, generate a first prediction model based upon the first set of time series features to predict levels of demand for the content item at the content distribution node at future time periods, identify, via the first prediction model, a first time period of the future time periods when a predicted level of demand for the content item exceeds a threshold level of demand, identify a second time period of the future time periods when a predicted level of utilization of the communication network is below a threshold level of utilization, the second time period being prior to the first time period, and transfer the content item to the content distribution node in the second time period.
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公开(公告)号：US20210304044A1
公开(公告)日：2021-09-30
申请号：US16833989
申请日：2020-03-30
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Vijayan Nagarajan , Abhay Dabholkar , Parth Sutaria
Abstract: Concepts and technologies disclosed herein are directed to optimizing and reducing redundant dispatch tickets via a network knowledge graph. According to one aspect disclosed herein, a network knowledge graph generation system (“NKGGS”) can construct a machine learning model to determine a probability of an installation job needing a helper job to fulfill a service order. The NKGGS can execute the machine learning model to determine the probability. The machine learning model can determine the probability of the installation job needing the helper job to fulfill the service order based upon a network knowledge graph and a dependency score. The NKGGS can cluster the installation job with a plurality of installation jobs.
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公开(公告)号：US11038940B2
公开(公告)日：2021-06-15
申请号：US16518229
申请日：2019-07-22
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Rudolph Mappus , Vijayan Nagarajan , Sheldon Meredith
Abstract: A processing system including at least one processor may collect a first set of time series features relating to requests for a content item at a content distribution node in a communication network, generate a first prediction model based upon the first set of time series features to predict levels of demand for the content item at the content distribution node at future time periods, identify, via the first prediction model, a first time period of the future time periods when a predicted level of demand for the content item exceeds a threshold level of demand, identify a second time period of the future time periods when a predicted level of utilization of the communication network is below a threshold level of utilization, the second time period being prior to the first time period, and transfer the content item to the content distribution node in the second time period.
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公开(公告)号：US11954609B2
公开(公告)日：2024-04-09
申请号：US16833989
申请日：2020-03-30
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Vijayan Nagarajan , Abhay Dabholkar , Parth Sutaria
Abstract: Concepts and technologies disclosed herein are directed to optimizing and reducing redundant dispatch tickets via a network knowledge graph. According to one aspect disclosed herein, a network knowledge graph generation system (“NKGGS”) can construct a machine learning model to determine a probability of an installation job needing a helper job to fulfill a service order. The NKGGS can execute the machine learning model to determine the probability. The machine learning model can determine the probability of the installation job needing the helper job to fulfill the service order based upon a network knowledge graph and a dependency score. The NKGGS can cluster the installation job with a plurality of installation jobs.
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5.发明授权公开(公告)号：US11842258B2
公开(公告)日：2023-12-12
申请号：US18070823
申请日：2022-11-29
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Mark Austin , Lauren Savage , Joshua Whitney , Abhay Dabholkar , Vijayan Nagarajan
CPC classification number: G06N20/20 , G06F18/217 , G06F18/2113 , G06F18/251 , G06V10/80 , G06V10/96
Abstract: Aspects of the subject disclosure may include, for example, receiving a plurality of proposed machine learning solutions to a machine learning problem including receiving, for each respective proposed machine learning solution of the plurality of proposed machine learning solutions, one or more of a machine learning model, a dataset and a data pipeline output; automatically determining hybrid solutions to the machine learning problem, including combining, by the processing system, at least one of a first component from a first proposed machine learning solution with at least one of a second component from a second proposed machine learning solution; and ranking the hybrid solutions including determining a log loss score for each hybrid solution and sorting the hybrid solutions according to the log loss score for each hybrid solution. Other embodiments are disclosed.
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Patent Agency Ranking
公开(公告)号：US11374994B2
公开(公告)日：2022-06-28
申请号：US17347408
申请日：2021-06-14
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Rudolph Mappus , Vijayan Nagarajan , Sheldon Meredith
IPC: H04L65/60 , H04L67/10 , H04L43/0876
Abstract: A processing system including at least one processor may collect a first set of time series features relating to requests for a content item at a content distribution node in a communication network, generate a first prediction model based upon the first set of time series features to predict levels of demand for the content item at the content distribution node at future time periods, identify, via the first prediction model, a first time period of the future time periods when a predicted level of demand for the content item exceeds a threshold level of demand, identify a second time period of the future time periods when a predicted level of utilization of the communication network is below a threshold level of utilization, the second time period being prior to the first time period, and transfer the content item to the content distribution node in the second time period.
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公开(公告)号：US20200184405A1
公开(公告)日：2020-06-11
申请号：US16212393
申请日：2018-12-06
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Rudolph Mappus , Vijayan Nagarajan , Abhay Dabholkar , Billy Langston, JR. , Matthew Rosenbloom , Quan Nguyen
Abstract: A processing system may obtain historical job feature, network plant feature, and calendar feature data associated with customer premises installation jobs of a telecommunication network which include a dispatch of a customer premises technician. A plurality of the customer premises installation jobs may further include a dispatch of a network-based technician. The processing system may then generate, from the historical data, a prediction model for predicting whether additional customer premises installation jobs may include a dispatch of a network-based technician, apply the prediction model to job feature, network plant feature, and calendar feature data associated with a pending customer premises installation job, determine a likelihood score of whether the pending customer premises installation job may include a dispatch of a network-based technician based upon the prediction model, and assign the pending customer premises installation job to at least a first customer premises technician in accordance with the likelihood score.
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公开(公告)号：US20220318545A1
公开(公告)日：2022-10-06
申请号：US17222412
申请日：2021-04-05
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Tyler Poff , Vijayan Nagarajan , Abhay Dabholkar , Abhishek Mishra
Abstract: Techniques for processing of electronic documents comprising tables to desirably extract and/or recreate tables, including information in the tables, are presented. A document processing management component (DPMC) can perform a multi-stage process to extract a table from a document and recreate the table, including the table structure and information, in an editable form. During first stage, DPMC can identify candidate cells of the table based on analysis of the document, including identifying border lines that can represent cell borders, identifying any free floating candidate cells, and identifying characters of the candidate cells. During second stage, DPMC can determine structural relationships between respective candidate cells and respective neighbor candidate cells in all directions, based on applicable rules, and record the respective associations between those candidate cells. During third stage, DPMC can determine row/column placement and scaling of the candidate cells based on the respective associations and applicable rules.
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公开(公告)号：US20210306395A1
公开(公告)日：2021-09-30
申请号：US17347408
申请日：2021-06-14
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Rudolph Mappus , Vijayan Nagarajan , Sheldon Meredith
Abstract: A processing system including at least one processor may collect a first set of time series features relating to requests for a content item at a content distribution node in a communication network, generate a first prediction model based upon the first set of time series features to predict levels of demand for the content item at the content distribution node at future time periods, identify, via the first prediction model, a first time period of the future time periods when a predicted level of demand for the content item exceeds a threshold level of demand, identify a second time period of the future time periods when a predicted level of utilization of the communication network is below a threshold level of utilization, the second time period being prior to the first time period, and transfer the content item to the content distribution node in the second time period.
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公开(公告)号：US10805901B1
公开(公告)日：2020-10-13
申请号：US16799253
申请日：2020-02-24
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Rudolph Mappus , Vijayan Nagarajan , Abhishek Mishra
Abstract: A processing system collects data points, wherein each data point indicates a location of user equipment in a telecommunication service provider network at a point in time, generates, for a first data point, a set of features over a plurality of time windows, wherein the set includes time series features, estimates an importance of each feature, wherein the importance indicates an accuracy with which the feature allows for estimation of an unseen location of the user equipment, wherein the unseen location is a location that is not identified in the plurality of data points, selects a threshold number of features from the set, wherein the threshold number of features have a greatest importance relative to all features in the set, generates a plurality of predictions of the unseen location, using the threshold number of features, aggregates the plurality of predictions, and estimates the unseen location based on the aggregating.
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