Patent ID: 11972861
Assignee: nan
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 3:
4. A computer implemented method of dynamic allocation of a plurality of resources to a plurality of remote location or at least one central location using an ensemble of machine learning (ML) models, comprising:
receiving, as an outcome of a central ML model, a prediction of a plurality of central location parameters of the at least one central location, wherein the central ML model is trained on a central training dataset of historic dynamically adjusted central location parameters;
iterating for each respective resource of the plurality of resources:
receiving, as an outcome of a respective remote ML model of a plurality of remote ML models, a prediction of a plurality of remote location parameters of a certain remote location, wherein each respective remote ML model corresponds to a respective resource and to one certain remote location, wherein the respective remote ML model is trained on a respective remote training dataset of historic remote location parameters;
inputting the plurality of central location parameters of the at least one central location and the plurality of remote location parameters of the certain remote location of the respective resource into a main ML model; and
obtaining, as an outcome of the main ML model, an allocation of the respective resource to the remote location or to the at least one central location for at least one future time interval, wherein a subset of a plurality of resources are allocated to respective remote locations and another subset of the plurality of resources are allocated to at least one central location,
wherein the main ML model is trained on a main training dataset including: training values for the plurality of central location parameters of the central location, and for each of a plurality of sample resources, training values for the plurality of remote location parameters, and corresponding labels of allocation to the remote location or to the central location for each of at least one historical time intervals.