US 12,182,677 B1Granted

Apparatus and methods for conditioning raw data based on temporal interpolations to generate optimal extrapolations of an entity

AssigneeThe Strategic Coach Inc.

InventorsBarbara Sue Smith, Daniel J. Sullivan

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Abstract

An apparatus for conditioning raw data based on temporal interpolations to generate optimal extrapolations of an entity, comprising a processor and a memory containing instructions configuring the processor to receive raw data associated with a temporal element from an entity condition the raw data, wherein conditioning the raw data includes clustering the raw data into primary clusters, wherein clustering the raw data includes dividing each primary cluster into a plurality of secondary clusters each contains a corresponding temporal interpolation, and ranking the plurality of secondary clusters as a function of the corresponding temporal interpolations, determine an extrapolation for each secondary clusters of the ranked plurality of secondary clusters based on the conditioned raw data, and generate a progression model as a function of the extrapolations.

Claims20

Dependent claims

An apparatus for conditioning raw data based on temporal interpolations to generate optimal extrapolations of an entity, wherein the apparatus comprises:

at least a processor;
and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive raw data associated with a temporal element from an entity;
condition the raw data, wherein conditioning the raw data comprises:
clustering the raw data into at least two primary clusters, wherein clustering the raw data into the at least two primary clusters comprises dividing each primary cluster into a plurality of secondary clusters, wherein each secondary cluster of the plurality of secondary clusters comprises a corresponding temporal interpolation;
and
ranking the plurality of secondary clusters as a function of the corresponding temporal interpolations, wherein ranking the plurality of secondary clusters comprises:
assigning a weight to the corresponding temporal interpolation of each secondary cluster of the plurality of secondary clusters as a function of spacing between data points in the corresponding temporal interpolation of each secondary clusters;
and
ranking the plurality of secondary clusters as a function of the assigned weights;
determine an extrapolation for each secondary cluster of the ranked plurality of secondary clusters based on the conditioned raw data;
and
generate a progression model as a function of the extrapolations.

11 more elements

A method for conditioning raw data based on temporal interpolations to generate optimal extrapolations of an entity, wherein the method comprises:

receiving, by at least a processor, raw data associated with a temporal element from an entity;
conditioning, by the at least a processor, the raw data, wherein conditioning the raw data comprises:
clustering the raw data into at least two primary clusters, wherein clustering the raw data into the at least two primary clusters comprises:
dividing each primary cluster into a plurality of secondary clusters, wherein each secondary cluster of the plurality of secondary clusters comprises a corresponding temporal interpolation;
and
ranking the plurality of secondary clusters as a function of the corresponding temporal interpolations, wherein ranking the plurality of secondary clusters comprises:
assigning a weight to the corresponding temporal interpolation of each secondary cluster of the plurality of secondary clusters as a function of spacing between data points in the corresponding temporal interpolation of each secondary clusters;
and
ranking the plurality of secondary clusters as a function of the assigned weights;
determining, by the at least a processor, an extrapolation for each secondary cluster of the ranked plurality of secondary clusters based on the conditioned raw data;
and
generating, by the at least a processor, a progression model as a function of the extrapolations.

9 more elements

Dependencies are inferred from the claim text.