Causal Inference for Resource Allocation Optimization

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Solution Overview

Problem

Existing resource allocation methods in computing systems are inefficient as they fail to accurately determine the optimal resource allocation to resource-receiving entities, leading to suboptimal utilization of finite resources and prioritization challenges.

Innovation Solution

A method using a resource allocation machine learning framework that generates non-linear causal effect predictions based on historical data and directed acyclic graph data, determining optimal causal variable values for resource-receiving entity cohorts through supervised machine learning regression, to configure optimal resource allocation and operation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional resource allocation methods are used, then the system is simple to operate, but the resource allocation efficiency and utilization are poor

Engineering Contradiction:

Improveresource allocation efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical/rules-based resource allocation systems with a machine learning-based causal inference system. The ML model processes historical data and directed acyclic graphs to predict causal effects of resource allocation, automatically determining optimal resource distribution without manual intervention or complex rule-based systems.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces a machine learning model as an intermediary between resource allocation decisions and outcome measurement. This intermediary (the causal inference model) processes complex relationships between resource allocation and outcomes, enabling efficient optimization without direct complex control mechanisms.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If more resource-receiving entities are served, then the coverage and utility are improved, but the resource consumption increases

Engineering Contradiction:

Improveservice coverageVSAvoidresource consumption

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The patent changes the parameters of resource allocation by using causal effect predictions to determine optimal allocation amounts. Instead of uniform or rule-based allocation, the system adjusts resource quantities based on predicted causal effects for different entities, enabling efficient resource distribution across multiple entities while minimizing total consumption.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If resource allocation is optimized for specific entities, then the outcome quality is improved, but the measurement and detection complexity increases

Engineering Contradiction:

Improveoutcome measurement accuracyVSAvoidcausal effect detection complexity

Core Design Contradiction:

Measurement precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The patent substitutes complex manual measurement and detection of causal effects with an automated machine learning model. The model processes historical data and directed acyclic graphs to infer causal relationships, providing precise outcome measurements without requiring complex detection mechanisms or manual analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20240211779A1Machine learning techniques for maintaining optimum number of resources for distrubution to selected entities based on non-causal inference

Publication Date: 2024.06.27 OPTUM SERVICES IRELAND LTD

AI summary

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for improving allocation of limited resources by determining an optimal amount of resources to allocate to resource-receiving entities in each of one or more resource-receiving entity cohorts based on non-linear causal effect predictions, and determining an optimum operation configuration based on the determined optimal amount of resource. Non-linear causal effect of selected amounts of resources assigned to specific resource-receiving entities are predicted on an outcome of interest associated with the resource-receiving entities.