US 12,190,030 B2
Apparatus and method for electronic system component determination and selection
Daniel Augusto Betts, Parkland, FL (US); and Juan Fernando Betts, Parkland, FL (US)
Assigned to PREDICTIVEIQ LLC, Boston, MA (US)
Filed by PREDICTIVEIQ LLC, Boston, MA (US)
Filed on Nov. 6, 2020, as Appl. No. 17/091,411.
Claims priority of provisional application 62/931,539, filed on Nov. 6, 2019.
Prior Publication US 2021/0133377 A1, May 6, 2021
Int. Cl. G06F 30/27 (2020.01); G06F 3/0482 (2013.01); G06F 30/17 (2020.01); G06F 111/20 (2020.01); G06Q 30/0601 (2023.01)
CPC G06F 30/27 (2020.01) [G06F 3/0482 (2013.01); G06F 30/17 (2020.01); G06Q 30/0621 (2013.01); G06Q 30/0641 (2013.01); G06F 2111/20 (2020.01)] 14 Claims
OG exemplary drawing
 
1. A computing device comprising:
a memory storing instructions;
a database; and
at least one processor communicatively coupled to the memory and to the database, the at least one processor configured to execute the instructions to:
for each of a plurality of components of a system:
compare each of a plurality of feature values for each of a plurality of component options to corresponding optimum feature values; and
based on the comparisons, determine a ranking of the plurality of component options;
based on the rankings, generate a model library comprising the plurality of component options for each of the plurality of components;
store the model library in the database;
provide for display one or more icons characterizing the plurality of components of the system;
receive, from an input/output device, a selection of one of the one or more icons;
based on the selected icon, determine a component of the plurality of components of the system;
generate input data characterizing at least one input to, and at least one output from, the component;
obtain, from a database, machine learning model data characterizing an executable modeling process for the component;
execute the machine learning model for the component based on the input data identifying at least one input to, and at least one output from, the component, wherein the executed machine learning model generates output data characterizing a predicted output of the component;
determine at least one parameter for the component based on the generated output data characterizing the predicted outputs for the component;
search the model library within the database to determine at least a portion of the plurality of component options for the component based on the at least one parameter and the ranking of the plurality of component options for the component;
provide for display an icon that facilitates a purchase of one or more of the plurality of component options;
in response to a selection of the icon by a user, display a purchase page that allows the user to enter information and purchase the selected component; and
receive, from the user, the information and complete the purchase.