Abstract: A computer system receives a training data set that includes representations of a population of entities that are associated with a target action. Each representation in the training data set including a set of attributes of a respective entity and a time the target action associated with the respective entity occurred. The computer system segments the training data set into multiple time periods, then trains a model to identify, for each of the multiple time periods, an attribute that is correlated with a likelihood that the target action will occur with respect to an entity having the identified attribute during a corresponding time period. The computer system uses the model to perform a first operation for a first set of entities in a first time period and perform a second operation for a second set of entities in a second time period.

Abstract: A computer system receives a training data set that includes representations of a population of entities that are associated with a target action. Each representation in the training data set including a set of attributes of a respective entity and a time the target action associated with the respective entity occurred. The computer system segments the training data set into multiple time periods, then trains a model to identify, for each of the multiple time periods, an attribute that is correlated with a likelihood that the target action will occur with respect to an entity having the identified attribute during a corresponding time period. The computer system uses the model to perform a first operation for a first set of entities in a first time period and perform a second operation for a second set of entities in a second time period.

Abstract: Various embodiments discussed herein are directed to improving existing technologies by generating a natural language sequence, which is a candidate for a first person to utter or not utter at least partially responsive to and based on a detected natural language utterance of a second person. A first score indicative of customer satisfaction is determined based on the content of the detected natural language utterance and learning patterns or associations within historical transcripts between, for instance, a customer and a customer service agent. Based on the level of customer satisfaction, the natural language sequence is generated.