Method, system, and computer readable medium for labeling and distributing products having multiple versions with recipient version correlation on a per user basis

A system comprising:

• a processor;
• a storage device;
• and
• a memory in communication with the processor, the memory storing program instructions, the processor operative with the program instructions to perform the operations of:
• receiving a plurality of panels of label characteristics for a first product, each panel of label characteristics being associated with one or more label characteristics each having a value characterizing a label for the first product;
• building a plurality of different executable machine learning models, each of the plurality of machine learning models being trained on at least a subset of the received panels of label characteristics for the first product;
• storing the plurality of trained machine learning models in a storage device;
• receiving an indication of a selection of one or more second products, each of the second products having an associated defined panel of label characteristics;
• executing at least one of the plurality of trained machine learning models using the panels of label characteristics associated with the products in the received indication of the selection of the one or more second products as inputs to the at least one trained machine learning model execution to generate an output derived from the panel of label characteristics associated with the selection of one or more second products, wherein the plurality of different executable machine learning models includes at least one of a Gaussian Mixture Model (GMM) to generate an algorithmic version of a panel for a product in the received indication of the selection of the one or more second products and the at least one of the plurality of trained machine learning models executed is the GMM, where an input to the GMM is a panel for a product in the received indication of the selection of the one or more second products and the GMM generates the algorithmic version of the panel for a product in the received indication of the selection of the one or more second products that fits within a distribution of label characteristics defined for the panels of label characteristics associated with the products in the received indication of the selection of the one or more second products;
• and
• storing a result set based on the generated output in a database, wherein the generated algorithmic version of the panel for a product in the received indication of the selection of the one or more second products for the second product item defines a panel template that is used as a basis to design a label.

A non-transitory computer readable medium having stored therein instructions that when executed cause a computer to perform a method comprising:

• receiving a plurality of panels of label characteristics for a first product, each panel of label characteristics being associated with one or more label characteristics each having a value characterizing a label for the first product;
• building a plurality of different executable machine learning models, each of the plurality of machine learning models being trained on at least a subset of the received panels of label characteristics for the first product;
• storing the plurality of trained machine learning models in a storage device;
• receiving an indication of a selection of one or more second products, each of the second products having an associated defined panel of label characteristics;
• executing at least one of the plurality of trained machine learning models using the panels of label characteristics associated with the products in the received indication of the selection of the one or more second products as inputs to the at least one trained machine learning model execution to generate an output derived from the panel of label characteristics associated with the selection of one or more second products, wherein the plurality of different executable machine learning models includes at least one of a Gaussian Mixture Model (GMM) to generate an algorithmic version of a panel for a product in the received indication of the selection of the one or more second products and the at least one of the plurality of trained machine learning models executed is the GMM, where an input to the GMM is a panel for a product in the received indication of the selection of the one or more second products and the GMM generates the algorithmic version of the panel for a product in the received indication of the selection of the one or more second products that fits within a distribution of label characteristics defined for the panels of label characteristics associated with the products in the received indication of the selection of the one or more second products;
• and
• storing a result set based on the generated output in a database, wherein the generated algorithmic version of the panel for a product in the received indication of the selection of the one or more second products defines a panel template that is used as a basis to design a label.