Adobe, Inc.

Chris Challis

Sunav Choudhary

William Brandon George

Wei Zhang

Fan Du

Shiv Kumar Saini

Ritwik Sinha

John Bevil Bates

Sanket Vaibhav Mehta

Said Kobeissi

Ivan Andrus

Aishwarya Asesh

Prithvi Bhutani

Harvineet Singh

Determination of high value customer journey sequences is performed by determining customer interactions that are most frequent as length N=1 sub-sequences, recursively determining most frequent length N+1 sub-sequences that start with the length N sub-sequences, determining a first count indicating how often one of the sub-sequences appears in the sequences, determining a second count indicating how often the one sub-sequence resulted in the goal, and using the counts to determine the most or least effective sub-sequences for achieving the goal.

Intelligent customer journey mining and mapping

The present disclosure describes systems, non-transitory computer-readable media, and methods for determining latent components of a metrics time series and identifying anomalous data within the metrics time series based on one or both of spikes/dips and level changes from the latent components satisfying significance thresholds. To identify such latent components, in some cases, the disclosed systems account for a range of value types by intelligently subjecting real values to a latent-component constraint for decomposing the time series and intelligently excluding non-real values from the latent-component constraint. The disclosed systems can further identify significant anomalous data values from latent components of the metrics time series by jointly determining whether one or both of a subseries of a spike-component series and a level change from a level-component series satisfy significance thresholds.

Robust anomaly and change detection utilizing sparse decomposition

The present disclosure relates to a method for detecting impermissible account sharing among user accounts of a streaming media service including the steps of determining a plurality of locations accessed by a given user account of the user accounts; determining a device access count for each of the locations, the device access count indicating how many times the corresponding location was accessed by at least one device associated with the given user account; identifying one of the locations having the highest device access count as a base location; calculating a risk coefficient for each remaining location; generating a sharing score for the given user account by summing the risk coefficients; and determining impermissible account sharing of the given user account has occurred when the sharing score exceeds a threshold.

Automatic identification of impermissable account sharing

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Alpine, UT, United States of America

Chris Challis