US 12,229,599 B2
Algorithmically optimized determination of resource assignments in machine request analyses
Robin Andrew Cecil Reid, Bradford (GB); Geoffrey John Cawood, Harrogate (GB); Nicholas James Taylor, Harrogate (GB); and Samantha Oxley, Harrogate (GB)
Assigned to Certinia Inc., Austin, TX (US)
Filed by Certinia Inc., Austin, TX (US)
Filed on Mar. 29, 2022, as Appl. No. 17/707,767.
Prior Publication US 2023/0315524 A1, Oct. 5, 2023
Int. Cl. G06F 9/50 (2006.01)
CPC G06F 9/5027 (2013.01) 19 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving at a computing platform a request comprising resource request data and criteria data;
evaluating the resource request data and the criteria data to identify a resource type and to generate a suitability matrix, the evaluating comprising invoking an analytical module configured to generate a data model using the resource request data, the criteria data, the resource type, and the suitability matrix;
selecting an algorithm to apply to the data model using the suitability matrix, the resource request data, the criteria data, and the resource type;
evaluating an output from the algorithm being applied to the data model to generate a resultant dataset, including evaluating another resultant dataset generated by applying another algorithm to another data model generated using the resource request data, the criteria data, the resource type, and the suitability matrix;
generating an optimization cost associated with the resultant dataset, the resultant dataset including the optimization cost;
using a simulated annealing algorithm to randomly generate further data indicating a selection of one or more resources using the resource request data and the criteria data, the selection being assigned the optimization cost; and
transmitting from a resource manager module in data communication with the computing platform to a client the resultant dataset identifying a resource, the resultant dataset being configured to at least be rendered on a display.