Labeling apparatus

Systems and methods are provided for receiving a data set that includes demographic data and population data; training, based on the data set, a neural network to establish a relationship between different physical layouts of messages and responses to the different physical layouts of the messages; applying the trained neural network to a user profile to predict a physical layout of a message; generating instructions for an electronic device based on the predicted physical layout of the message, the instructions comprising the message; and transmitting the instructions to the electronic device to create a physical label having a layout corresponding to the predicted physical layout of the message.

BACKGROUND

The present disclosure relates generally to the technical field of electronic labeling devices.

BRIEF SUMMARY

Systems and methods are provided for receiving a data set that includes demographic data and population data; training, based on the data set, a neural network to establish a relationship between different physical layouts of messages and responses to the different physical layouts of the messages; applying the trained neural network to a user profile to predict a physical layout of a message; generating instructions for an electronic device based on the predicted physical layout of the message, the instructions comprising the message; and transmitting the instructions to the electronic device to create a physical label having a layout corresponding to the predicted physical layout of the message.

DETAILED DESCRIPTION

FIG.1illustrates a labeling system100in accordance with one example. The labeling system100includes a neural network102, a processor106and an electronic device104. The processor106receives a data set that includes demographic data and population data. The processor106provides the data set to the neural network102.

The neural network102is trained, based on the data set received from the processor106, to establish a relationship between different physical layouts of messages and responses to the different physical layouts of the messages. The neural network102is applied to a user profile to predict a physical layout of a message.

As an example, if the user should receive a savings message per the data sets and analytics, but has not responded to prior messages per a set limit or factor, then the second-best intervention message would be communicated by the processor106. As part of machine learning, the system also interprets population behaviors to the intervention messages and determines if similar populations sets are likely to respond, e.g., after two identical intervention messages, versus four in order to optimize when to be persistent, versus default to the secondary opportunity.

Example supervised learning modules include linear regression and decision trees, such as those trained to explain dependent variables using explanatory variables. Through the training process, parameters or weighting may be revised to minimize a loss function and make predictions as correct as possible.

The predicted physical layout of the message is provided to the processor106. The processor106generates instructions, that include the message, for the electronic device104based on the predicted physical layout of the message. The processor106transmits the instructions to the electronic device104to create a physical label having a layout corresponding to the predicted physical layout of the message. The electronic device104outputs the physical label. The electronic device104can include an electronic labeling device. Certain examples of the labeling system100are discussed in greater detail in commonly-owned Andy Hahn et al. U.S. patent application Ser. No. 16/209,701, filed on Dec. 4, 2018, which is hereby incorporated by reference in its entirety and in commonly-owned Andy Hahn et al. U.S. Patent Application No. 62/594,522, filed on Dec. 4, 2017, which is hereby incorporated by reference in its entirety.

FIG.2illustrates a routine200for generating labels in accordance with one example. Routine200is performed by the labeling system100discussed above in connection withFIG.1.

In block202, routine200receives a data set that includes demographic data and population data. In block204, routine200trains, based on the data set, a neural network to establish a relationship between different physical layouts of messages and responses to the different physical layouts of the messages. In block206, routine200applies the trained neural network to a user profile to predict a physical layout of a message. In block208, routine200generates instructions for an electronic device based on the predicted physical layout of the message, the instructions comprising the message. In block210, routine200transmits the instructions to the electronic device to create a physical label having a layout corresponding to the predicted physical layout of the message.

In the example embodiment, the labels are rectangular. In other embodiments, the labels may be of a different size and/or shape. A user with a high risk of non-adherence will be sent an adherence message. If that user is down to zero refills, a refill message will be sent instead. The need for refills takes precedence or is higher priority because the refill is required for adherence.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention cover the modifications and variations of this invention provided that they come within the scope of any claims and their equivalents.