Efficient generation of an extrapolated inference associated with a subsequent event with increased fidelity and reduced redundancy

A method for generating an extrapolated indication associated with a subsequent event executed by an integrated module comprising an AI processor accelerated by an AI framework, the integrated module comprising a front-end determination program and a back-end indication program, the method comprising:

• receiving, by the back-end indication program of the integrated module at a computing station, input data including data associated with the subsequent event and indicative of at least one determined variable for the subsequent event, wherein the at least one determined variable is strongly correlated with indications of a plurality of previous events and is identified utilizing bivariate analysis and a plurality of data brackets, each data bracket of the plurality of data brackets correlated with indications of the plurality of previous events and identified utilizing a multiple variable statistical algorithm and determination data indicative of the plurality of previous events associated with a plurality of users;
• generating, by the back-end indication program of the integrated module, the extrapolated indication associated with the subsequent event, the generating including utilizing only a portion of the input data that is indicative of the at least one determined variable, together with the at least one determined variable and a determined value for each determined variable of the at least one determined variable, wherein the determined value for each determined variable separates ranges of data of the associated determined variable, which is strongly correlated with indications of the plurality of previous events within the determination data and is generated utilizing bivariate analysis and the plurality of data brackets;
• wherein generating the extrapolated indication associated with the subsequent event utilizing only the portion of the input data indicative of the at least one determined variable and each associated determined value increases fidelity of the extrapolated indication and reduces redundancy within the extrapolated indication relative to an extrapolated indication generated utilizing input data of a data bracket not indicating the at least one determined variable for the subsequent event, and thereby reduces a number of computations performed by the AI processor, reducing processing time and computing power consumption and increasing available processing resources for other computer readable instructions.