Automatic customer complaint resolution
First Claim
1. One or more non-transitory computer-readable media storing computer-executable instructions that upon execution cause one or more processors to perform acts comprising:
- receiving performance data regarding user device and network components of a wireless carrier network from multiple data sources;
receiving an indication of an issue affecting one or more user devices that are using the wireless carrier network, the issue impacting a quality of service experienced by at least one user of the one or more user devices as the one or more user devices are used to make voice calls, make multimedia calls, upload data, or download data using the wireless carrier network;
tracking geolocations of a user as the user roams between the geolocations with a user device during a particular time interval;
analyzing at least a portion of the performance data related to the geolocations that are roamed by the user with the user device during the particular time interval using a trained machine learning model to determine a root cause for the issue affecting the one or more user devices, the trained machine learning model employing multiple types of machine learning algorithms to analyze the performance data; and
providing at least one of the root cause and a solution that resolves the root cause for presentation.
1 Assignment
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Accused Products
Abstract
An analytic application may automatically determine a root cause of an issue with a wireless carrier network and generate a solution for the root cause. Initially, a data management platform may receive performance data regarding user device and network components of a wireless carrier network from multiple data sources. Subsequently, the analytic application may receive an indication of an issue affecting one or more user devices that are using the wireless carrier network. The analytic application may analyze the performance data using a trained machine learning model to determine a root cause for the issue affecting the one or more user devices. The trained machine learning model may employ multiple types of machine learning algorithms to analyze the performance data. The analytic application may provide the root cause or the solution that resolves the root cause for presentation.
30 Citations
20 Claims
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1. One or more non-transitory computer-readable media storing computer-executable instructions that upon execution cause one or more processors to perform acts comprising:
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receiving performance data regarding user device and network components of a wireless carrier network from multiple data sources; receiving an indication of an issue affecting one or more user devices that are using the wireless carrier network, the issue impacting a quality of service experienced by at least one user of the one or more user devices as the one or more user devices are used to make voice calls, make multimedia calls, upload data, or download data using the wireless carrier network; tracking geolocations of a user as the user roams between the geolocations with a user device during a particular time interval; analyzing at least a portion of the performance data related to the geolocations that are roamed by the user with the user device during the particular time interval using a trained machine learning model to determine a root cause for the issue affecting the one or more user devices, the trained machine learning model employing multiple types of machine learning algorithms to analyze the performance data; and providing at least one of the root cause and a solution that resolves the root cause for presentation. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 19)
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11. A computer-implemented method, comprising:
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receiving, at a data management platform executing on one or more computing nodes, performance data regarding user device and network components of a wireless carrier network from multiple data sources, the performance data includes one or more of network component performance data, user device performance data, social media data, alarm data, trouble ticket data, or key performance indicator data generated by a network monitoring tool; receiving, at an analytic application executing on the one or more computing nodes, an indication of an issue affecting one or more user devices that are using the wireless carrier network, the issue impacting a quality of service experienced by at least one user of the one or more user devices as the one or more user devices are used to make voice calls, make multimedia calls, upload data, or download data using the wireless carrier network, the indication being a subscriber or a network monitoring application generated trouble ticket; tracking geolocations of a user as the user roams between the geolocations with a user device during a particular time interval; analyzing, via the analytic application executing on the one or more computing nodes, at least a portion of the performance data related to the geolocations that are roamed by the user with the user device during the particular time interval using a trained machine learning model to determine a root cause for the issue affecting the one or more user devices, the trained machine learning model employing multiple types of machine learning algorithms to analyze the performance data; and providing, via the analytic application executing on the one or more computing nodes, at least one of the root cause and a solution that resolves the root cause for presentation. - View Dependent Claims (12, 13, 14, 15, 16, 17, 20)
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18. A system, comprising:
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one or more processors; and memory including a plurality of computer-executable components that are executable by the one or more processors to perform a plurality of actions, the plurality of actions comprising; performing feature engineering on a training corpus for generating a machine learning model that is used to determine solutions for issues with a wireless carrier network; training an initial type of machine learning algorithm using the training corpus to generate the machine learning model; determining a training error measurement of the machine learning model is above a training error threshold; selecting an additional type of machine learning algorithm based on a magnitude of the training error measurement according to one or more algorithm selection rules in response to the training error measurement being above the training error threshold; training the additional type of machine learning algorithm using the training corpus to generate training results; augmenting the machine learning model with the training results from the additional type of machine learning algorithm; determining that generation of a trained machine learning model is complete when additional training error measurement of the machine learning model following the augmenting is at or below the training error threshold; receiving performance data regarding user device and network components of the wireless carrier network from multiple data sources; receiving an indication of an issue affecting one or more user devices that are using the wireless carrier network, the issue impacting a quality of service experienced by at least one user of the one or more user devices as the one or more user devices are used to make voice calls, make multimedia calls, upload data, or download data using the wireless carrier network; analyzing the performance data using the trained machine learning model to determine a root cause for the issue affecting the one or more user devices, the trained machine learning model employing multiple types of machine learning algorithms to analyze the performance data; and providing at least one of the root cause and a solution that resolves the root cause for presentation.
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Specification