| CPC G06Q 30/0629 (2013.01) [G06Q 30/0635 (2013.01)] | 17 Claims |
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1. A computer-implemented method for analyzing a plurality of items, wherein:
a first trained model trained on a first training data set comprising names, descriptions, and categories of the plurality of items, a second trained model trained on a second training data set comprising primary categories for items, a third trained model trained on a third training a set comprising acceptable and unacceptable pairings of items, and a fourth trained model trained on a fourth training data set comprising rejection rates of items when offered as substitutes are arranged in a pipeline such that outputs of earlier trained models in the pipeline are provided as inputs to later trained models in the pipeline, and
the method comprises:
identifying a target item;
filtering, using the first trained model, the plurality of items to obtain at least one similar item to the target item, and outputting the at least one similar item;
providing the at least one similar item output from the first trained model as input to the second trained model;
identifying, using the second trained model, a primary category of the target item and a primary category of the at least one similar item, and outputting the primary category of the target item and the primary category of the at least one similar item;
providing the at least one similar item output from the first trained model, the primary category of the target item from the second trained model, and the primary category of the at least one similar item from the second trained model, as input to the third trained model;
identifying, using the third trained model, potential substitutes for the target item based on the at least one similar item, the primary category of the target item, and the primary category of the at least one similar item;
providing the potential substitutes to the fourth trained model; and
ranking, using the fourth trained model, the potential substitutes based on a rejection probability of each of the potential substitutes,
wherein the first trained model, second trained model third trained model, and fourth trained model are configured to generate ent temporal intervals.
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