At&t Intellectual Property I, L.p.

At&t Mobility Ii LLC

Rudolph Mappus

Vijayan Nagarajan

James Morgan White

Sheldon Kent Meredith

Jeremy Fix

Herani Soewarto Brotman

Ravishankar Doejode

Chad Blackwell

Surekha Padala

Abhishek Mishra

Abhay Dabholkar

George Goehring

Barry Karesh

Mark Morris

Billy Langston, Jr

Matthew Rosenbloom

Quan Nguyen

Sang Han

The disclosed technology is directed towards automatically detecting failure states and the cause of the failure. For a network, the technology collects status messages from equipment and customers into batches as they occur. The technology groups and aggregates messages, then transforms the aggregations to the frequency domain. Anomalies induce detectable changes in the particle distribution of a trained particle filter, from which an anomalous spectrogram is generated. The status messages of each device are iteratively removed from the larger set of messages, resulting in reduced subsets that are each aggregated, transformed into a modified spectrogram and compared against the anomalous spectrogram to obtain a distance score. The distance score for each device is used to rank the devices with respect to being the cause of the failure.

Identifying and localizing equipment failures

Devices, non-transitory computer-readable media and methods for providing a haptic signal based upon a detection of an object are disclosed. For example, the processor of a device may transmit a first ultrasonic signal via an ultrasonic emitter and detect an object based upon a receiving of a reflected signal via an ultrasonic detector, where the reflected signal comprises a reflection of the first ultrasonic signal from the object. The processor may further monitor for a second ultrasonic signal via the ultrasonic detector. The device may be associated with a user, and the second ultrasonic signal may be associated with a teammate of the user. The processor may also select whether to provide an instruction to a haptic actuator when the object is detected, based upon whether the second ultrasonic signal is received via the ultrasonic detector.

Wearable ultrasonic sensors with haptic signaling for blindside risk detection and notification

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.

Streaming content cache scheduling

A method for generating a multi-layer predictive model includes collecting historical observable data from one or more pieces of equipment of a same type, wherein the historical observable data is collected at different hierarchical levels of the one or more pieces of equipment; collecting operational state indications of the pieces of equipment corresponding to the collected historical observable data; generating, from the collected historical observable data, a set of operational state models, wherein each operational state model corresponds to one of the different hierarchical levels; and generating, from outputs of the set of operational state models, a top-level operational model for the piece of equipment. The top-level operational model is operable to determine maintenance and replacement timing for the piece of equipment.

Systems and method for management and allocation of network assets

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.

Telecommunication network customer premises service dispatch optimization

A processing system may obtain a loan graph including a travel cost for network technicians between adjacent work centers of a telecommunication network, each network technician assigned to a work center, and obtain work quantities for each work center for a day, at least a first work center having excess work for network technicians assigned to the first work center. The processing system may select, in accordance with an objective function, moves by a first subset of network technicians comprising moves to adjacent work centers from the work centers to which the first subset is assigned, select in accordance with the objective function, moves by a second subset of network technicians comprising moves to adjacent work centers from work centers to which the second subset is assigned, and generate a work plan for the day for the network technicians in accordance with the moves.

Telecommunication network technician scheduling via multi-agent randomized greedy search

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.

User equipment localization through time series search

Determining a location of a user equipment (UE) based on historical location data and historical sector transition data is disclosed. A correlation between historic location information and a historic sector transition can be determined. The correlation can be stored in a searchable data set. A location of a current UE can be inferred based on a sector transition of the current UE. The sector transition of the current UE can be searched against the data set to indicate a likely location of the current UE based on historical information. The searchable data set can be based on sparse location data enabling location determinations for a current UE that can otherwise lack location services. Moreover, an order of a sector transition can imbue a directionality to stored location information such that a likely location in a sector can be correlated to a transition from a prior sector of a network session of the UE.

Inferring user equipment location data based on sector transition

More efficient mobile device location data can be obtained by estimating a most likely location point in a coverage pattern using a kernel density estimation technique. The kernel density estimation technique can provide a continuous estimate of the most frequented locations of a mobile device(s) within a coverage area. For each wireless sector, the collected location data can grouped to the closest geographic coordinate system, and an inference can be made based on the grouped data.

Facilitation of mobile device geolocation

Determining a location of a user equipment (UE) based on historical location data and historical sector transition data is disclosed. A correlation between historic location information and a historic sector transition can be determined. The correlation can be stored in a searchable data set. A location of a current UE can be inferred based on a sector transition of the current UE. The sector transition of the current UE can be searched against eh data set to indicate a likely location of the current UE based on historical information. The searchable data set can be based on sparse location data enabling location determinations for a current UE that can otherwise lack location services. Moreover, an order of a sector transition can imbue a directionality to stored location information such that a likely location in a sector can be correlated to a transition from a prior sector of a network session of the UE.

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Plano, TX, United States of America

Rudolph Mappus