Using artificial intelligence to design a product

A system for optimizing computer machine learning, comprising:

• a communications interface configured to:
• receive an optimization goal;
• and
• receive a selection of a base option among one or more matching base option candidates based in part on a ranking of the base option among the one or more matching base option candidates, wherein the base option is ranked based in part on a divergence between actual performance that includes measured sales data of the base option and performance that includes expected sales data of the base option predicted by one or more machine learning prediction models, wherein the base option has a score above a threshold score, wherein the base option and the one or more matching base option candidates are ranked based on their corresponding scores;
• a processor configured to:
• utilize the one or more machine learning prediction models selected based at least in part on the optimization goal to determine prediction values associated with alternative features for the selected base option, wherein the one or more machine learning prediction models were trained using training data to at least identify machine learning weight values associated with the alternative features for the one or more machine learning prediction models;
• based on the prediction values, sort at least a portion of the alternative features to generate an ordered list of at least the portion of the alternative features for the selected base option;
• and
• provide the ordered list for use in manufacturing an alternative version of the selected base option with one or more of the alternative features in the ordered list.

A computer program product for optimizing computer machine learning, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:

• receiving an optimization goal;
• using the optimization goal to search a database of base option candidates to identify one or more matching base option candidates that at least in part matches the optimization goal;
• receiving a selection of a base option among the one or more matching base option candidates based in part on a ranking of the base option among the one or more matching base option candidates, wherein the base option is ranked based in part on a divergence between actual performance that includes measured sales data of the selected base option and performance that includes expected sales data of the base option predicted by one or more machine learning prediction models, wherein the base option has a score above a threshold score, wherein the base option and the one or more matching base option candidates are ranked based on their corresponding scores;
• utilizing the one or more machine learning prediction models selected based at least in part on the optimization goal to determine prediction values associated with alternative features for the selected base option, wherein the one or more machine learning prediction models were trained using training data to at least identify machine learning weight values associated with the alternative features for the one or more machine learning prediction models;
• based on the prediction values, sorting at least a portion of the alternative features to generate an ordered list of at least the portion of the alternative features for the selected base option;
• and
• providing the ordered list for use in manufacturing an alternative version of the selected base option with one or more of the alternative features in the ordered list.