| CPC G06Q 10/0631 (2013.01) [G06F 16/29 (2019.01); G06F 18/22 (2023.01); G06N 20/00 (2019.01); G06Q 10/0833 (2013.01); G06Q 40/02 (2013.01)] | 20 Claims |
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1. A method comprising:
receiving, via a computer network, a natural language request for delivery of a payment card to a delivery address, wherein the payment card is to be delivered from one of a plurality of delivery source locations;
executing a machine learning (ML) model trained based on a plurality of rules and training data, the ML model trained to determine a respective weight for each of a plurality of factors based on the plurality of rules, the plurality of factors comprising: a) a distance of each of the plurality of delivery source locations to a plurality of addresses, b) a plurality of detectable internet connections associated with a plurality of internal customers at each of the delivery source locations, c) a detectable data bandwidth associated with a plurality of enterprise internet connections at each of the plurality of delivery source locations, d) an amount of consumption of one or more utility types by each of the delivery source locations, e) a plurality of staff transactions at each of the delivery source locations, and f) a detectable street traffic associated with a plurality of external customers entering each of the plurality of delivery source locations;
determining a subset of the plurality of delivery source locations that are within a threshold distance of the delivery address;
selecting, via execution of the ML model, a first delivery source location for the payment card from the subset of the plurality of delivery source locations based on:
i) computing a threshold score for each of the plurality of delivery source locations, wherein the threshold score is related to a security compliance measure associated with the payment card, and
ii) computing an aggregated score for each of the delivery source locations in the subset, the aggregated scores based on the respective weights of each of the plurality of factors applied to a) a respective distance of each delivery source location of the subset to the delivery address, b) the plurality of detectable internet connections associated with the plurality of internal customers at each delivery source location of the subset, c) the detectable data bandwidth associated with the plurality of enterprise internet connections the delivery source location of the subset, d) the amount of consumption of one or more utility types by the delivery source location of the subset, e) the plurality of staff transactions at the delivery source location of the subset, and f) the detectable street traffic associated with the plurality of external customers entering the delivery source location of the subset;
instructing, via the network, impression machinery at the first delivery source location to manufacture the payment card; and
instructing, via the network, a courier service to deliver the payment card to the delivery address based on the selection of the first delivery source location and the plurality of rules,
wherein the ML model is continuously trained using traffic training data comprising dynamically updated street traffic information of transportation routes associated with the plurality of delivery source locations, at least a portion of the traffic training data obtained via one of satellite data or surveillance equipment associated with the plurality of delivery source locations.
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