GENERATIVE METAMODEL FOR ACCELERATED SIMULATION MODELING

Number: 20260170328
Published: 2026-06-18
Filed: 2025-12-12
Assignee: DISNEY ENTERPRISES, INC.
Inventors: ZAHRN; Frederick C., HEINS; Kevin Andrew, YUAN; Wei, DONG; Yijun
CPC: G06N3/08; G06N3/044; G06N3/0442; G06N3/045; G06N3/047; G06N3/088
Verdict: Set aside dropped in weekly review
Source: Google Patents · FreePatentsOnline

The keeper's note

Abstract

The present invention sets forth techniques for training a metamodel to mimic the operation of a simulation that calculates engagement values based on a quantity of services, a product type that consumes those services, a service date on which the services are to be consumed, and a time horizon of days prior to and including the service date. For each date in the time horizon, the simulation calculates an engagement value that represents the cumulative percentage of the services that is simulated to have been allocated as of that date. The metamodel infers engagement values for each date in the time horizon based on the same inputs provided to the simulation. The metamodel is iteratively trained based on differences between the simulated and inferred engagement values associated with multiple sets of inputs. The trained metamodel may require much less time and far fewer computing resources compared to the simulation.

Background

BACKGROUND Field of the Various Embodiments

The present invention relates generally to simulation and machine learning and, more specifically, to a generative metamodel for accelerated simulation modeling. Description of the Related Art

An organization may allocate services or other resources to be shared by a variety of products. On any date, the organization's capacity to deliver services is finite, and the resources may be shared among different products that each have differing service needs and characteristics. As a result, optimally managing the availability of resources across potentially varying time horizons is a key organizational challenge. Managing service availability generally improves the quality of provided services and prevents wasting or spoilage of services that are available but not successfully allocated to one or more products that require the services. Managing service availability is further complicated by dynamic changes in either or both of service capacity and service demand.

Analytical challenges in service management include causal inference, forecasting, and optimization. Causal inference attempts to quantify the effect of a particular condition or event on service demand and/or capacity, enabling informed adjustments in response to the condition or event. Forecasting identifies and projects trends in service demand and capacity, and aids in future service allocation planning. Optimization is used to determine the best produc

Claims

1. A computer-implemented method for training a neural network, comprising: determining training input associated with a base simulation model, wherein the training input includes at least a capacity metric associated with a service and a designation of a product that consumes the service; generating, via execution of a neural network, training output based on the training input, wherein the training output comprises predictions of one or more engagement curves associated with the training input; and training the neural network, based on one or more losses associated with (i) a first distribution associated with the training output and (ii) a second distribution associated with a simulation output of the base simulation model. || 11. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: determining training input associated with a base simulation model, wherein the training input includes at least a capacity metric associated with a service and a designation of a product that consumes the service; generating, via execution of a neural network, training output based on the training input, wherein the training output comprises predictions of one or more engagement curves associated with the training input; and training the neural network, based on one or more losses associated with (i) a first distribution associated with the training output and (ii) a second distribution associated with a simulation output of the base simulation model. || 18. A system comprising: one or more memories storing instructions; and one or more processors for executing the instructions to: determine training input associated with a base simulation model, wherein the training input includes at least a capacity metric associated with a service and a designation of a product that consumes the service; generate, via execution of a neural network, training output based on the training input, wherein the training output comprises predictions of one or more engagement curves associated with the training input; and train the neural network, based on one or more losses associated with (i) a first distribution associated with the training output and (ii) a second distribution associated with a simulation output of the base simulation model.