Abstract: System derives training change factors for services provided for training product user, priority assigned to training service ticket initiated by training product user, times of service ticket interactions associated with training service ticket, and/or age of training service ticket, and also for times of states of training service ticket. System uses training service ticket and training change factors to train change-based machine-learning model to predict change-based training probability that training product user escalated service for training service ticket. System derives change factors for services provided for product user, priority assigned to service ticket initiated by product user, times of service ticket interactions associated with service ticket, and/or age of service ticket, and also for times of states of training service ticket.

Abstract: System derives training change factors for services provided for training product user, priority assigned to training service ticket initiated by training product user, times of service ticket interactions associated with training service ticket, and/or age of training service ticket, and also for times of states of training service ticket. System uses training service ticket and training change factors to train change-based machine-learning model to predict change-based training probability that training product user escalated service for training service ticket. System derives change factors for services provided for product user, priority assigned to service ticket initiated by product user, times of service ticket interactions associated with service ticket, and/or age of service ticket, and also for times of states of training service ticket.

Abstract: Predicting end-of-life support deprecation is described. A system receives an identification of a product, and then identifies support request records associated with the product. The system identifies support information associated with the support request records. A predictive model uses the support information to predict a deprecation for the product. The system outputs the prediction of the deprecation for the product.

Abstract: System trains machine learning model to determine content data, metadata, and context data for support ticket communications, in response to receiving support ticket communications. Machine learning model receives communication associated with support ticket, and determines content data, metadata, and context data for communication. System converts content data, metadata, and context data for communication into first impulse for first channel and second impulse for second channel. System determines first channel value based on first type of conversion of first impulse and any impulses for first channel that are converted from data that is determined for support ticket event. System determines second channel value based on second type of conversion of second impulse and any impulses for second channel that are converted from data that is determined for support ticket event. System uses first channel value and second channel value to generate priority associated with support ticket, and outputs priority.

Abstract: System receives historical text block, creates historical features for historical text block's historical text lines. System trains machine-learning model to cluster historical features into historical features clusters based on their similarities. System identifies historical features cluster as historical human text cluster. System classifies each historical text line for historical human text cluster as human text, and each historical text line for other historical features clusters as machine text. System receives text block, creates features for text block's text lines. System applies trained machine-learning model to cluster features into features clusters based on their similarities. System identifies features cluster as human text cluster. System classifies each text line for human text cluster as human text, and each text line for other features clusters as machine text.

Abstract: A support ticket summarizer, similarity classifier, and resolution forecaster are described. A system trains a machine learning model to identify topic sequences for support ticket communications, identify topic sequences that are classified as similar, and predict subsequent topics for multiple support ticket communications, in response to receiving the support ticket communications. The machine learning model receives a communication for a support ticket, and then identifies a sequence of topics for the communication for the support ticket. The machine-learning model identifies historical sequences of topics, for historical support tickets, which are classified as similar to the sequence of topics. The machine-learning model uses the historical sequences of topics to predict at least one subsequent topic for the sequence of topics. The system outputs the at least one subsequent topic.

Abstract: Systems and methods are described for determining customer sentiment using natural language processing in technical support communications. Communication content exchanged between a customer device and an agent device may be filtered to remove technical support syntax. Using natural language processing techniques, the processor may assign baseline values to features within the filtered communication content. To assign the baseline values, features from the filtered communication content may be identified, where the features pertain to expressed sentiments, and a trained first model may be applied to identify polarities and strengths related to the identified features. A score value may then be assigned to each identified feature, the score values being based on the polarities and strengths. A subset of the score values may then be weighted based on metadata and/or context, and the score values may be combined using a second model to determine an overall sentiment of the filtered communication content.