Amazon Technologies, Inc.

Tarun Bhatia

Amber Roy Chowdhury

Valerie Galluzzi Liptak

Karthik Ram Srinivasan

Sourav Kumar Agarwal

Ravi Garg

George Henry Forman

Techniques for triggering a mitigation action based on an artificial intelligence (AI) model-based delivery defect prediction are described. In an example, a computer system determines contextual information associated with a stage of a delivery flow of an item to a delivery location. The computer system generates an input based at least in part on the contextual information and provides the input to an AI model trained to predict delivery defect information at delivery flow stages. The computer system determines, based at least in part on an output of the artificial intelligence model, a predicted delivery defect associated with the stage of the delivery flow. The computer system determining a mitigation action based at least in part on the predicted delivery defect and causing the mitigation action to be performed.

Triggering a mitigation action based on an artificial intelligence model-based delivery defect prediction

Techniques are described for predicting a future defect and triggering a mitigation action. In an example, a system generates a first input to a causal model based on geographical data associated with a location and area data associated with one or more areas that include the location, and determines a first output of the causal model. The first output indicates a first prediction of a cause of a past defect for a service associated with the location. The system also generates a second input to a predictive model based on the first input and the first output, and determines a second output of the predictive model. The second output indicates a second prediction of a future defect associated with the service. Based on the second prediction, a mitigation action can be performed such that the future defect is prevented.

Triggering a service mitigation action based on a causal-predictive system

Techniques for a geofence generation feature are described herein. Geospatial vector data for a geographical area and seed points for delivery locations within the geographical area may be obtained. A plurality of polygons that represent the geographical area may be determined based on an algorithm that uses the seed points and the geospatial vector data. A boundary point for each polygon may be determined based on coordinates for the plurality of polygons where each boundary point corresponds to a single seed point. A geofence of a first size may be generated, for each polygon, based on the coordinates for the polygons, the seed point, and the boundary point. Historical scans of GPS data may be obtained from user devices during historical visits to the geographical area. The geofence may be modified to a second size or a different shape based on the historical scans of the GPS data.

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Sunnyvale, CA, United States of America

Tarun Bhatia