Abstract: A system for reducing input data size for use in an artificial intelligence (AI) engine for predicting a subsequent event. The system includes a computer configured to implement instructions to receive input data and time data indicative of previous events associated with users. The instructions configure the system to determine interface channels associated with modes of interface with the users, previous event characteristics, or both. The system implements instructions associating previous event data with time windows. The instructions configure the system to generate user window values for the combinations of users and time windows. The user window values indicate the interface channels and previous event characteristics of data within the respective time windows. Implementing the instructions configures the system to form a first portion of the raw input data having an association value below a threshold with respect to preceding the subsequent event and generate condensed input data.

Abstract: A system includes a computer to implement a front-end input condensation program and a back-end machine learning program. Steps of the front-end program include receive input data and time data indicative of previous events associated with users; determine interface channels associated with modes of interface with the users and/or previous event characteristics; associate previous event data with time windows; generate user window values for the combinations of users and time windows indicating the interface channels and previous event characteristics of data within the respective time windows; and form condensed input data without raw input data having a low association with respect to preceding the subsequent event. Steps of the back-end program include receive the condensed input data and use the condensed data to generate an inference related to the subsequent event such that a time required by the machine learning algorithm to generate the inference is reduced.

Abstract: A system for reducing the time to train a machine learning program for predicting a subsequent event. The system includes a computer configured to implement instructions to receive training data and time data indicative of training events associated with users. The instructions configure the system to determine interface channels associated with modes of interface with the users, training event characteristics, or both. The system implements instructions associating training event data with time windows. The instructions configure the system to generate user window values for the combinations of users and time windows. The user window values indicate the interface channels and training event characteristics of data within the respective time windows. Implementing the instructions configures the system to form a first portion of the raw input data having an association value below a threshold with respect to preceding the subsequent event and generate condensed training data.