CPC G06Q 40/03 (2023.01) [G06F 16/288 (2019.01); G06N 20/00 (2019.01)] | 20 Claims |
1. A system for automatic data segmentation, the system comprising:
one or more processors; and
a non-transitory computer readable medium having stored thereon instructions that, when executed by the one or more processors, cause the one or more processors to:
receive, from a client system, input data associated with a first individual of a first plurality of individuals for whom a client system has provided a service;
input, into a hyperdimensional model, the input data, wherein the hyperdimensional model has been trained by a training engine to generate predicted recovery values by:
collecting historical data from the client system, wherein the historical data comprises historical input data and a plurality of actual recovery values, the historical input data and the plurality of actual recovery values corresponding to a second plurality of individuals for whom the client system has previously provided a service,
generating first training data based on the historical data, wherein the hyperdimensional model comprises a plurality of dimensions, and wherein each dimension in the plurality of dimensions corresponds to a variable of the first training data, and
training the hyperdimensional model using the first training data;
receive, from the hyperdimensional model, a predicted recovery value for the first individual, wherein the predicted recovery value is a weighted average of a predicted unit yield and a predicted recovery rate for the first individual;
receive, from the client system, a plurality of segment boundary definitions that define a plurality of segments, where each of the plurality of segments correspond to a range of recovery values;
based on the predicted recovery value, assign the first individual to a first segment of the plurality of segments, wherein the predicted recovery value is within the range of recovery values corresponding to the first segment;
transmit, to the client system, an electronic message comprising the first segment;
receive, from the client system a first actual recovery value for the first individual;
update the plurality of actual recovery values to include the first actual recovery value; and
provide the updated plurality of actual recovery values to the training engine for additional training, retraining, or updating of the hyperdimensional model.
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