US 12,333,600 B2
Automatic data segmentation system
Christopher G. Busch, Maple Grove, MN (US); Nathaniel W. Lutz, Apple Valley, MN (US); and Zackary Dixon, Leander, TX (US)
Assigned to Experian Health, Inc., Franklin, TN (US)
Filed by Experian Health, Inc., Franklin, TN (US)
Filed on Dec. 1, 2023, as Appl. No. 18/527,040.
Application 18/527,040 is a continuation of application No. 16/521,267, filed on Jul. 24, 2019, abandoned.
Claims priority of provisional application 62/702,646, filed on Jul. 24, 2018.
Prior Publication US 2024/0212041 A1, Jun. 27, 2024
Int. Cl. G06Q 40/03 (2023.01); G06F 16/28 (2019.01); G06N 20/00 (2019.01)
CPC G06Q 40/03 (2023.01) [G06F 16/288 (2019.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
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.