US 12,333,616 B1
Computer systems and methods for determining environment impact indicators for food products
Lucas Gevaerd Cava, Curitiba (BR); Andrew Carlos Kondlatsch, Rio Negro (BR); Jeanny Zimeri Franz, Budd Lake, NJ (US); Meng Sun, Easton, PA (US); Qiaoxuan Zhou, East Hanover, NJ (US); and Hsiu Wei Yang, Atlantic Highlands, NJ (US)
Assigned to Intercontinental Great Brands LLC, East Hanover, NJ (US)
Filed by INTERCONTINENTAL GREAT BRANDS LLC, East Hanover, NJ (US)
Filed on Oct. 31, 2024, as Appl. No. 18/933,550.
Int. Cl. G06F 11/30 (2006.01); G06Q 50/02 (2012.01); G06Q 50/06 (2012.01)
CPC G06Q 50/02 (2013.01) [G06Q 50/06 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computing platform comprising:
at least one network interface;
at least one processor;
at least one non-transitory computer-readable medium; and
program instructions stored on the at least one non-transitory computer-readable medium that, when executed by the at least one processor, cause the computing platform to:
extract a first source dataset from a first database table containing data about product-level ingredients, wherein the first source dataset comprises (i) rows representing data records for a given set of product-level ingredients, wherein each respective product-level ingredient in the given set is included in a corresponding food product and (ii) columns representing data variables that, for each respective product-level ingredient in the given set, provide respective information about the respective product-level ingredient;
extract a second source dataset from a second database table containing data about food products, wherein the second source dataset comprises (i) rows representing data records for a given set of food products and (ii) columns representing data variables that, for each respective food product in the given set, provide respective information about the respective food product;
merge the first source dataset and the second source dataset into a first merged dataset that comprises (i) rows representing data records for the given set of product-level ingredients and (ii) columns representing data variables that, for each respective product-level ingredient in the given set, provide (a) respective information about the respective product-level ingredient and (b) respective information about the corresponding food product in which the respective product-level ingredient is included;
update the first merged dataset by inserting an additional column representing a data variable that, for each respective product-level ingredient in the given set, provides a respective measure of a dry mass of the respective product-level ingredient within the corresponding food product in which the respective product-level ingredient is included;
extract a third source dataset from a third database table containing environmental-impact values for ingredients, wherein the third source dataset comprises (i) rows representing data records for a given set of ingredients and (ii) columns representing data variables that, for each respective ingredient in the given set, provide respective environmental-impact values for the respective ingredient;
merge the updated first merged dataset and the third source dataset into a second merged dataset that comprises (i) rows representing data records for the given set of product-level ingredients and (ii) columns representing data variables that, for each respective product-level ingredient in the given set, provide (a) respective information about the respective product-level ingredient, (b) respective information about the corresponding food product in which the respective product-level ingredient is included, (c) a respective measure of the dry mass of the respective product-level ingredient within the corresponding food product in which the respective product-level ingredient is included, and (d) respective environmental-impact values for the respective product-level ingredient; and
determine a respective group of environmental-impact indicators for each respective product-level ingredient in the given set using the second merged dataset.