Sondermind Inc.

Hanjie Shen

Vincent Mysliwiec

Xiao Yang

Jonathan Knights

Holly Dubois

In some embodiments, a method includes receiving longitudinal interaction data. The longitudinal interaction data is sorted into a plurality of bins. Using a run-length encoder, at least one inactivity period indication is generated, and an inferred period indication is generated based on the at least one inactivity period indication. The method also includes (1) generating, based on the plurality of bins, a plurality of activity metrics and (2) sorting, using a clustering model and based on the plurality of activity metrics, the plurality of bins to produce an inactivity cluster. An expected period indication and a characteristic value are generated based on (1) the inferred period indication and (2) the expected period indication. A series of actions is determined by providing, via the processor, the characteristic value as input to a machine learning model.

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Issaquah, WA, United States of America

Hanjie Shen