US 11,961,099 B2
Utilizing machine learning for optimization of planning and value realization for private networks
Kevin Edward Kapich, Broomfield, CO (US); Sean Delaney, Hoboken, NJ (US); Jorge Andres Gomez Fuentes, Austin, TX (US); Lina Christensen, Denver, CO (US); and Tariq Salameh, Dubai (AE)
Assigned to Accenture Global Solutions Limited, Dublin (IE)
Filed by Accenture Global Solutions Limited, Dublin (IE)
Filed on Mar. 15, 2021, as Appl. No. 17/249,820.
Prior Publication US 2022/0292529 A1, Sep. 15, 2022
Int. Cl. G06Q 30/0202 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); H04L 41/14 (2022.01); H04L 41/147 (2022.01)
CPC G06Q 30/0202 (2013.01) [G06N 5/04 (2013.01); G06N 20/00 (2019.01); H04L 41/145 (2013.01); H04L 41/147 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving, by a device, network data, business data, and user configuration data associated with an entity that is a candidate for a private network;
training a classification machine learning model, a first linear regression machine learning model, a second linear regression machine learning model, and an observation machine learning model based on observations;
processing, by the device, the business data and the user configuration data, with the classification machine learning model, to determine a network hardware equipment prediction for the private network;
processing, by the device, the network data and the business data, with the first linear regression machine learning model, to determine a business output prediction for the private network,
wherein the business output prediction includes one or more of:
a prediction of capital expenditures associated with the private network, or
a prediction of operational expenditures associated with the private network;
utilizing, by the device, the second linear regression machine learning model to determine a data consumption prediction for the private network based on the network hardware equipment prediction;
processing, by the device, the network hardware equipment prediction, the business output prediction, and the data consumption prediction, with the observation machine learning model, to determine a financial profitability prediction for the entity based on deployment of the private network; and
performing, by the device, one or more actions based on the financial profitability prediction;
wherein performing the one or more actions comprises:
receiving, by the device, feedback based on the financial profitability prediction; and
retraining, by the device and utilizing the feedback as additional training data, one or more of the classification machine learning model, the first linear regression machine learning model, the second linear regression machine learning model, or the observation machine learning model based on the financial profitability prediction.