System and method for automatic subject line generation

A computer-implemented method for processing machine-generated subject lines, comprising:

• requesting, via a graphical user interface, information comprising a specified type and description of an electronic campaign;
• receiving, by a server, said information related to the electronic campaign;
• preprocessing, by the server, the received information for sending to a generative text engine model, wherein the preprocessing comprises checking formatting, removing keywords, and ensuring the user has provided sufficient information;
• sending to the generative text engine model the preprocessed received information, wherein the generative text engine model is operable to return text based on a given text prompt;
• receiving a plurality of N subject lines generated based on the preprocessed received information from the generative text engine model;
• reducing, by the server, the N subject lines down to M subject lines, wherein the reducing comprises:
• generating similarity scores by embedding subject lines as vectors using vectorization methods and computing, for each pair of subject lines, a cosine similarity score;
• eliminating one of the pair based on whether the cosine similarity score exceeds a first similarity threshold;
• computing cosine similarity scores between at least one subject line and the merchant-provided description using the vectorization methods;
• eliminating one or more subject lines when a cosine similarity score between a subject line and the merchant-provided description exceeds a second similarity threshold different from the first similarity threshold;
• and
• wherein when the reducing results in less than a threshold number of subject lines, resending a request to the generative text engine model to generate additional subject lines;
• assigning, by a trained second discriminator model, a second quality rating for each of the M subject lines, wherein the second discriminator model was trained based on historical tracked customer user actions including opens and clicks that included a displayed subject line;
• and
• reducing the M subject lines to K subject lines based on the second quality rating;
• applying a fine-tuned enhancer model to enhance existing content for a selected one or more of the K subject lines;
• displaying K subject lines with enhanced content to a merchant user;
• displaying select subject lines to customer users at the customer user devices;
• tracking customer user actions based on the select subject lines displayed to the customer users at the customer user devices, wherein the customer user devices operate alone or optionally, in conjunction with a server and/or merchant server to sense customer user actions;
• analyzing the customer user actions responsive for success metrics corresponding to the displayed subject lines including open rates and click rates;
• carrying out a first training, wherein the first training comprises training, through a discriminator improvement cycle, the second discriminator model to assign a second quality rating to a subject line based on the analyzed customer user actions including open rates and click rates, and updating the second discriminator model at the conclusion of the discriminator improvement cycle;
• and
• carrying out a second training, wherein the second training comprises training. through a text generation improvement cycle, the generative text engine model based on subject lines meeting a second quality rating threshold as updated by the second discriminator model during the first training, and updating the generative text engine model at the conclusion of the text generation improvement cycle.