Verizon Patent and Licensing Inc.

Le Su

Krishna Pichumani Iyer

Ye Ouyang

Wenyuan Lu

Christopher M Schmidt

Carol Becht

Maulik Shah

Shaun Robert Pola

Khurram Abbas

Steven F Rice

Dandan Wang

Zhenyi Lin

Yiming Chen

Lin Tang

Garrison William Brainard

Lun Yin

Dwight Ellis Woods

Yu Wan

A system described herein may provide a technique for using modeling techniques to identify events, trends, etc. in a set of data, such as streaming video or audio content. The system may perform lightweight pre-processing operations on a different set of data, such as object position data, to identify timeframes at which an event may potentially have occurred, and the modeling techniques may be performed at portions of the streaming content that correspond to such timeframes. The system may forgo performing such modeling techniques at other portions of the streaming content, thus conserving processing resources.

Systems and methods for reduced resource utilization for event modeling

A device may include a processor configured to collect historical location data for a user equipment (UE) device associated with a user and determine a movement pattern for the user based on the collected historical location data. The processor may be further configured to obtain current location data for the UE device over a time period; determine a mobility score for the user for the time period based on the determined movement pattern and the obtained current location data, wherein the mobility score is associated with the user's movements during the time period with respect to the determined movement pattern for the user; and provide the determined mobility score for the user to another device configured to adjust one or more parameters of a radio access network (RAN) associated with the UE device based on the determined mobility score.

Systems and methods to improve network performance in wireless networks using user mobility patterns

A device may collect network performance data associated with a user equipment of a network. The network performance data may include information associated with a plurality of performance indicators of the network. The device may process information associated with a first portion of the plurality of performance indicators to determine a first performance category experience score, and information associated with a second portion of the plurality of performance indicators to determine a second performance category experience score. The device may process the first performance category experience score and the second performance category experience score to determine a network experience score. The device may determine whether the network experience score satisfies a threshold value. The device may perform one or more actions based on determining that the network experience score satisfies the threshold value.

Systems and methods for evaluating a user experience in a network based on performance indicators

A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Network anomaly detection and network performance status determination

A Self-Organizing Network (SON) collects data pertaining to a first number of cells of a wireless network. The SON splits the collected data into a second number of groups, and, for each of the second number of groups, repeatedly set a third number of clusters to a different number between a low limit and a high limit. The SON, for each of the settings, clusters the cells into the third number of clusters and trains a deep neural network to perform a regression analysis on the third number of clusters. For each of the second number of groups, the SON also determines an optimum number of clusters based on the regression analyses, re-clusters the cells into the optimum number of clusters; and tunes engineering parameters based on the re-clustering to optimize performance of the wireless network and quality of experience pertaining to the wireless network.

Automated intelligent self-organizing network for optimizing network performance

A method, a device, and a non-transitory storage medium for estimating voice call quality include performing automatic speech recognition, for each of a plurality of voice calls, to generate recognized text for both an originating device acoustic signal and a receiving device acoustic signal. The recognized text for both the originating device acoustic signal and the receiving device acoustic signal are compared to the reference text to identified recognition errors and a voice call quality score for each of the originating device acoustic signal and the receiving device acoustic signal are determined. A correlation between the network conditions and the voice call quality scores is then determined.

Methods and systems for evaluating voice call quality

A method, a device, and a non-transitory storage medium provide receiving a plurality of voice call quality values and values for a plurality key performance indicators (KPIs) related to voice over Wi-Fi voice call quality; selecting a subset of the plurality of KPIs based on a correlation between each KPI and the voice call quality value; performing a plurality of discrete regression analyses based on the subsets of the plurality of KPIs and the voice call quality values to generate a plurality of regression results; determining an accuracy for each of the plurality of regression results; assigning weights to each of the plurality of regression results based on the determined accuracies; and combining the plurality of regression results using the assigned weights to generate a final combined estimated VoWiFi voice call quality algorithm that accurately predicts the voice call quality value based on values for the selected subset of the plurality of KPIs.

Growing community of inventors

Edison, NJ, United States of America

Le Su