Personal Predictive Analytics - Project aims to build new model in deep learning through classification of static defined and dynamic unknowable matrices and vectors. Through new relationships in generational advancement, divergence and convergence in adversarial random tree, vector fields can be used to project new observational projections from various points of reference. Intersections of these projections create a volume in a confidence interval that is a mix of static and defined information, as well as variable undefined. As More oberservational instersections narrow the undefined volumes, actionable intelligence can be gleaned, and disseminated on an individuals timely needs.