CPC G06Q 10/0833 (2013.01) [G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06T 7/579 (2017.01); G06T 19/006 (2013.01); H04W 4/023 (2013.01); H04W 4/024 (2018.02); H04W 4/026 (2013.01); H04W 4/027 (2013.01); H04W 4/38 (2018.02); H04W 4/80 (2018.02)] | 20 Claims |
1. A non-transitory computer-readable medium having program instructions stored thereon executable by at least one hardware processor that, when executed, direct the at least one hardware processor to:
receive peripheral device data from at least one peripheral device associated with a delivery driver, the peripheral device comprising at least one sensor configured to generate the peripheral device data;
determine, through execution of a machine learning routine, a current activity and a current location of the delivery driver relative to a delivery destination;
in response to the delivery driver being within a threshold distance of the delivery destination, determine a current state of a delivery process through execution of a machine learning routine using the peripheral device data as an input to the machine learning routine;
transition a client application executing on an augmented reality device associated with the delivery driver between a plurality of states based at least in part on the current state of the delivery process, wherein the transition of the client application between states is performed without a manual interaction with the augmented reality device;
wherein at least one of the states of the client application comprises providing navigation directions in an augmented reality environment provided by the augmented reality device; and,
wherein:
in an instance in which the current activity indicates that the delivery driver is driving to a delivery destination, the machine learning routine utilizes a first machine learning model;
in an instance in which the current activity indicates that the delivery driver is seeking or retrieving the item from a cargo area of a vehicle, the machine learning routine utilizes a second machine learning model; and
in an instance in which the current activity indicates that the delivery driver is walking to or from a delivery destination to the vehicle, the machine learning routine utilizes a third machine learning model.
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