Outcome creation based upon synthesis of history

A device for identifying synthetic contributors required to generate a desired outcome, the device comprising:

• a special purpose machine learning server;
• a machine learning database electrically connected to the special purpose machine learning server;
• and
• a list of synthetic features stored in the special purpose machine learning server, where the synthetic features are features input to a machine learning model that can be controlled;
• wherein the special purpose machine learning server creates the machine learning model by operating a training module on the machine learning database on the machine learning server, accepts an input of the desired outcome, and uses the desired outcome in conjunction with an outcome creation engine to reverse engineer the machine learning model by parsing rules of the machine learning model using the desired outcome to derive a set of necessary-past attributes, the set of necessary-past attributes filtered through the list of synthetic features to identify the synthetic contributors required to generate the desired outcome, where the necessary-past attributes are data and pattern attributes that need to be present when the machine learning model is run to generate the desired outcome and the synthetic contributors are presence or value attributes of the machine learning model that can be made to act in a way to generate the desired outcome.

A device for generating a desired outcome, the device comprising:

• a special purpose machine learning server;
• and
• a machine learning database electrically connected to the special purpose machine learning server;
• wherein the special purpose machine learning server comprises:
• a means for creating a machine learning model by operating a training module on the machine learning database on the machine learning server;
• and
• a means for generating the desired outcome using the machine learning model.