Verizon Patent and Licensing Inc.

Senthil Muthusamy

Rajat Sharma

Vipul Jha

Sheikbharith Liakathali

Srinivasan Krishnamurthy

Travis R McLaren

Pritam Bedse

Sailesh K Mishra

Manah Mounir Khalil

Abhilash Paul

Adil S Belihomji

Safwan Aly

Ramasubramanian Radhakkrishnan

Shashidhar Hiremath

Mohammad Y Salman

Sudarshan Chavva

Albert Michael Selvin

Sunil Joshua Alexander Konaiyagarri

Somesh Sharma

Ehssan Ghanem

Naga Dumpala

Richard J Worthington

Nikhil Kommera

Philip A Jenkins

Phillip W Melancon

Venkateswararao Godavarti Veera

John Benjamin Lertola

Vinod Khanna Saini Ganesh

Ning Gao

Pavani Kamalapuram

Premjith A Pullamplavil

John Russo

Khagender Arrekuti

Raja Sri Swaroop Mallampalli

Rishi Kanth Chitti

A device may receive building location information associated with a multi-unit building. The device may obtain an image that depicts the multi-unit building. The device may process, using a building analysis model, the image to identify exterior access features of the multi-unit building. The building analysis model may be trained based on a plurality of historical images of other exterior access features. The device may determine, using a scoring system and based on a configuration of exterior access features that are identified by the building analysis model, an exterior accessibility score of the unit. The device may perform, based on the exterior accessibility score, an action associated with qualifying the unit for installation of a service that involves access, from the unit, to an exterior of the multi-unit building.

Systems and methods for identifying a service qualification of a multi-unit building based on exterior access

Techniques for network usage analysis and forecasting are disclosed. In one embodiment, a computerized method is disclosed comprising obtaining network access data identifying network access of a plurality of users, obtaining user data for the plurality of users, analyzing the obtained network access data, the analyzing comprising determining a network usage pattern for each user, determining a plurality of feature sets corresponding to the plurality of users, a feature set determined for a respective user comprising network usage features and user features determined using the user data obtained for the respective user, determining a number of clusters formed using the plurality of feature sets, each cluster grouping a number of users, of the plurality of users, having similar feature sets, and communicating information about at least one cluster in response to a network analysis request.

Computerized method and system for network usage analysis and forecasting

A device may receive parameters to test modifications to an application associated with a product and/or a service, and may process data identifying the parameters and the application, with a machine learning model, to generate test applications for testing corresponding modifications to the application. The device may define test group sizes of test groups for testing the test applications, and may receive, from user devices, requests for accessing the application. The device may assign, based on the test group sizes, sets of the user devices to the test groups for testing the test applications, and may provide the test applications concurrently to the corresponding sets of the user devices based on the test groups. The device may receive, from the corresponding sets of the user devices, feedback associated with the test applications, and may perform one or more actions based on the feedback.

Systems and methods for generating modified applications for concurrent testing

A device may receive, from a customer platform, customer event data identifying events occurring between customers and an entity, and may receive, from the customer platform, customer action data generated by machine learning models and identifying customer actions to be taken by the customer platform in response to the occurrence of the events. The device may receive, from the customer platform, customer results data identifying results of the customer actions taken by the customer platform, and may calculate current key performance indicators based on the customer event data, the customer action data, and the customer results data. The device may retrain one or more of the machine learning models based on the current key performance indicators to generate one or more retrained machine learning models, and may provide the one or more retrained machine learning models to the customer platform.

Systems and methods for measuring and validating key performance indicators generated by machine learning models

A platform can receive information regarding a user interaction, wherein the user interaction is associated with one or more channels that correspond to respective interfaces or media for the user interaction; retrieve supplemental information associated with the user interaction, wherein the supplemental information relates to at least one of: a state of a managed device associated with the user interaction, or a previous user interaction; identify, based on the information regarding the user interaction or the one or more channels, one or more models to process the information regarding the user interaction and the supplemental information, wherein the one or more models are identified from a plurality of models; determine, using the one or more models, an action to be performed with regard to the user interaction; and provide information identifying the action.

Interpretation of user interaction using model platform

A device may capture call data corresponding to call between an agent of a call center and a caller. The device may identify a guidance template based on the call data. The guidance template may include one or more rules and/or information for assisting the agent during the call. The device may generate an agent prompt based on the guidance template and/or provide the guidance prompt to an agent device of the agent. The agent device may receive the guidance prompt and display the guidance prompt to the agent. The agent device may capture additional call data from the call and update the guidance prompt based on the call inputs.

Systems and methods for providing real-time assistance to call center agents

Call center personnel, i.e., company agents may be monitored and information from monitoring the call center agents may be presented on a portable device. The information from monitoring the call center agents may include various key performance indexes (KPIs) that enable the performance of the call center agents to be assessed. The KPIs may be updated in near-real time based on monitoring pending calls handled by the agents. Sound analytics may also be performed on the calls. Information regarding the call center agents may be graphically organized by the portable device so that call centers agents who may need assistance may be easily identified. The portable device may join a call. For example, a setting for a session associated with the call may be modified to include the manager device in the session.

Call center agent management

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Lewisville, TX, United States of America

Senthil Muthusamy