US 2022/0035677 A1Published application

Dynamic allocation of resources in surge demand

AssigneeOptum Services (Ireland) Limited

InventorsVicente Ruben Del Pino Ruiz, Hendrik Kleine

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Abstract

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for optimized resource transformation given a set of resource optimization parameters. In accordance with one embodiment, a method is provided that includes: identifying a demand surge scenario associated with resource demand conditions; in response to identifying the scenario: determining a downgrade set of resources; determining whether a downgrade-only resource transformation scenario characterized by downgrade transformation of the downgrade set satisfies the conditions; and responsive to determining the downgrade-only resource transformation scenario fails to satisfy the conditions: identifying residual resources that are transformable to meet the conditions, processing the residual resources using a machine learning model characterized by the set of resource optimization parameters to generate resource priority scores, and generating a optimized resource transformation scenario from a scenario based at least in part on the resource priority scores and the downgrade-only resource transformation scenario.

Claims20

Dependent claims

A computer-implemented method for optimized resource transformation given a set of resource optimization parameters, the computer-implemented method comprising:

identifying, using one or more processors, a demand surge scenario, wherein the demand surge scenario is associated with one or more resource demand conditions;
in response to identifying the demand surge scenario:
determining, using the one or more processors and based at least in part on a plurality of resource transformation data objects associated with a plurality of resources, a downgrade set of the plurality of resources;
determining, using the one or more processors, whether a downgrade-only resource transformation scenario characterized by downgrade transformation of the downgrade set of the plurality of resources satisfies the one or more resource demand conditions;
in response to determining that the downgrade-only resource transformation scenario satisfies the one or more resource demand conditions, generating, using the one or more processors, an optimized resource transformation scenario based at least in part on the downgrade-only resource transformation scenario;
and
in response to determining that the downgrade-only resource transformation scenario fails to satisfy the one or more resource demand conditions:
(i) identifying, using the one or more processors, one or more residual resources of the plurality of resources that are deemed transformable to meet the one or more resource demand conditions but are not in the downgrade set, (ii) processing, using the one or more processors, the one or more residual resources using a resource optimization machine learning model that is characterized by the set of resource optimization parameters to generate one or more resource priority scores for the one or more residual resources, (iii) generating, using the one or more processors, a hybrid resource transformation scenario based at least in part on the one or more resource priority scores and the downgrade-only resource transformation scenario, and (iv) generating, using the one or more processors, the optimized resource transformation scenario based at least in part on the hybrid resource transformation scenario;
and
causing, using the one or more processors, one or more resource transformation actions to be performed based at least in part on the optimized resource transformation scenario.

7 more elements

An apparatus for optimized resource transformation given a set of resource optimization parameters, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the apparatus to at least:

identify a demand surge scenario, wherein the demand surge scenario is associated with one or more resource demand conditions;
in response to identifying the demand surge scenario:
determine, based at least in part on a plurality of resource transformation data objects associated with a plurality of resources, a downgrade set of the plurality of resources;
determine whether a downgrade-only resource transformation scenario characterized by downgrade transformation of the downgrade set of the plurality of resources satisfies the one or more resource demand conditions;
in response to determining that the downgrade-only resource transformation scenario satisfies the one or more resource demand conditions, generate an optimized resource transformation scenario based at least in part on the downgrade-only resource transformation scenario;
and
in response to determining that the downgrade-only resource transformation scenario fails to satisfy the one or more resource demand conditions:
(i) identify one or more residual resources of the plurality of resources that are deemed transformable to meet the one or more resource demand conditions but are not in the downgrade set, (ii) process the one or more residual resources using a resource optimization machine learning model that is characterized by the set of resource optimization parameters to generate one or more resource priority scores for the one or more residual resources, (iii) generate a hybrid resource transformation scenario based at least in part on the one or more resource priority scores and the downgrade-only resource transformation scenario, and (iv) generate the optimized resource transformation scenario based at least in part on the hybrid resource transformation scenario;
and
cause one or more resource transformation actions to be performed based at least in part on the optimized resource transformation scenario.

7 more elements

A non-transitory computer storage medium comprising instructions for optimized resource transformation given a set of resource optimization parameters, the instructions being configured to cause one or more computer processors to at least perform operations configured to:

identify a demand surge scenario, wherein the demand surge scenario is associated with one or more resource demand conditions;
in response to identifying the demand surge scenario:
determine, based at least in part on a plurality of resource transformation data objects associated with a plurality of resources, a downgrade set of the plurality of resources;
determine whether a downgrade-only resource transformation scenario characterized by downgrade transformation of the downgrade set of the plurality of resources satisfies the one or more resource demand conditions;
in response to determining that the downgrade-only resource transformation scenario satisfies the one or more resource demand conditions, generate an optimized resource transformation scenario based at least in part on the downgrade-only resource transformation scenario;
and
in response to determining that the downgrade-only resource transformation scenario fails to satisfy the one or more resource demand conditions:
(i) identify one or more residual resources of the plurality of resources that are deemed transformable to meet the one or more resource demand conditions but are not in the downgrade set, (ii) process the one or more residual resources using a resource optimization machine learning model that is characterized by the set of resource optimization parameters to generate one or more resource priority scores for the one or more residual resources, (iii) generate a hybrid resource transformation scenario based at least in part on the one or more resource priority scores and the downgrade-only resource transformation scenario, and (iv) generate the optimized resource transformation scenario based at least in part on the hybrid resource transformation scenario;
and
cause one or more resource transformation actions to be performed based at least in part on the optimized resource transformation scenario.

7 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/150,203Filed 15 Jan 2021
Priority: 28 Jul 2020
Published: 3 Feb 2022
Term ends: Not estimatedThe source records don’t support an estimate.

Classification10 codes

G06F 9/5038
The resource being a machine, e.g. CPUs, Servers, Terminals: considering the execution order of a plurality of tasks, e.g. taking priority or time…
G06Q 10/04
Administration; Management: forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or…
G06F 9/505
The resource being a machine, e.g. CPUs, Servers, Terminals: considering the load
G06F 9/5077
Partitioning or combining of resources: logical partitioning of resources; Management or configuration of virtualized resources

Family

Application63/057,385Us provisional application · filed 28 Jul 2020
US 11,645,119 B2Related patent
US 2022/0035677 A1This patent

From the US filing history. Foreign family members aren’t listed.

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