US 12,488,313 B2
Minimizing aggregate carbon footprint within geographical region
Sudhanshu Sekher Sar, Bangalore (IN); Sarbajit K. Rakshit, Kolkata (IN); Sudheesh S. Kairali, Kozhikode (IN); and Satyam Jakkula, Bangalore (IN)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Jun. 27, 2023, as Appl. No. 18/342,045.
Prior Publication US 2025/0005511 A1, Jan. 2, 2025
Int. Cl. G06Q 10/087 (2023.01)
CPC G06Q 10/087 (2013.01) 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
predicting, by one or more processors of a computer system, using execution of a first trained artificial intelligence model, a product demand of users located in a geographical region over a fixed time period, and delivery location clusters associated with the product demand within the geographical region, wherein
the predicting is based on feeding of input data associated with the users to the first trained artificial intelligence model;
inputting, by the one or more processors, the delivery location clusters into a regression model,
the regression model outputting recommended locations of temporary micro-warehouses within the geographical region based on the delivery location clusters;
training, by the one or more processors, a second artificial intelligence model based on a plurality of emission-based parameters, wherein the training of the second artificial intelligence model comprises:
learning, one or more parameters of the plurality of emission-based parameters that affect an aggregate carbon footprint caused by emissions resulting from transportation to and from the temporary micro-warehouses;
modifying, by the one or more processors, using the second trained artificial intelligence model, the recommended locations based on reducing the aggregate carbon footprint, wherein
the modified recommended locations correspond to optimized locations of the temporary micro-warehouses within the geographical region, and
the second trained artificial intelligence model uses the product demand output by the first trained artificial intelligence model, the recommended locations output from the regression model, and the plurality of emission-based parameters as inputs for the modifying of the recommended locations;
iteratively feeding, by the one or more processors, the optimized locations of the temporary micro-warehouses to the first trained artificial intelligence model along with the input data; and
re-executing the first trained artificial intelligence model based on the input data and the optimized locations of the temporary micro-warehouses, wherein
setting up of the temporary micro-warehouses is performed at the modified recommended locations, and
the temporary micro-warehouses are existing structures within the geographical region.