Verizon Patent and Licensing Inc.

Travis R McLaren

Farid Khafizov

Vittal N Gunturu

Wade W Hiney

Rajat Sharma

Senthil Muthusamy

Srinivasan Krishnamurthy

Pritam Bedse

Aleksandr Iakubovich

Sailesh K Mishra

Vipul Jha

Safwan Aly

John Benjamin Lertola

Vinod Khanna Saini Ganesh

Pavani Kamalapuram

John Russo

Khagender Arrekuti

Rishi Kanth Chitti

Richard J Worthington

Philip A Jenkins

Charles A Liscomb

Venkateswararao Godavarti Veera

A method for summarizing dialogs may include obtaining an agent text stream and a customer text stream, segmenting the agent text stream and customer text stream into sentences, and labeling sentences associated with the segmented agent text stream and the segmented customer text stream. The method may further include extracting sentences from the agent text stream and the customer text stream based upon frequencies of appearance of words and terms of interest; generating an agent summary paragraph based on the extracted sentences from the agent text stream, and generating a customer summary paragraph based on the extracted sentences from the customer text stream. The method may identify keywords associated with each of the agent summary paragraph and the customer summary paragraph.

Method and apparatus for summarization of dialogs

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 computer device may include a memory storing instructions and processor configured to execute the instructions to obtain a text file generated based on a communication session; select topics for classifying communication sessions; and apply one or more trained machine learning models to the obtained text file to determine one or more most likely topics for the communication session. The processor may be further configured to apply regular expression binary classifiers, each associated with a different topic, to the obtained text file to determine a likelihood the communication session is associated with a particular topic; select a topic for the communication session based on the determined one or more most likely topics and the determined likelihood the communication session is associated with the particular topic; and classify the communication session based on the selected topic.

Combining multiclass classifiers with regular expression based binary classifiers

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

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Southlake, TX, United States of America

Travis R McLaren