Nonlinear Causal Inference for Resource Allocation Optimization

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Solution Overview

Problem

Existing resource allocation techniques fail to optimize allocations of various types of resources due to an inability to account for interrelated effects on predictive outcomes, leading to inefficient and costly management.

Innovation Solution

The development of methods and systems that generate optimization functions using nonlinear causal modeling to determine optimal parameter occurrences of outcome-influencing data types for data objects, thereby optimizing predictive outcomes while minimizing predictive costs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If generic deterministic rules or associative frameworks are used for resource allocation, then the allocation process is simple and easy to implement, but the ability to account for interrelated effects of multiple resource types on predictive outcomes is lost, leading to inefficient allocation

Engineering Contradiction:

Improveease of allocation processVSAvoidresource allocation efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent replaces deterministic rules and associative frameworks with causal inference models that use probability theory and statistical learning. The system models causal relationships between resource allocations and predictive outcomes using structured data representations, enabling it to reason about interrelated effects of multiple resource types while maintaining computational tractability through optimized inference algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transforms the allocation approach by changing from fixed deterministic parameters to probabilistic causal parameters. The system learns causal effects from data and uses these to make adaptive allocation decisions, allowing the allocation strategy to dynamically adjust based on observed relationships between resource types and outcomes rather than following static rules.

Inventive Principle:

Principle #35Parameter changes

2Device complexity

If traditional resource allocation techniques are used, then the system complexity is low, but the ability to identify and account for relationships between allocations of various combinations of resource types and predictive outcomes is insufficient

Engineering Contradiction:

Improvesystem complexityVSAvoidpredictive outcome measurement accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent segments the complex causal inference problem into manageable components by representing causal relationships as structured data objects with specific fields for causal variables, outcomes, and confounders. This segmentation allows the system to handle complex interrelationships between multiple resource types while maintaining computational efficiency through modular processing of causal effects.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces causal inference models as intermediary components between resource allocation data and predictive outcomes. These models act as mediators that process the relationships between multiple resource types and outcomes, extracting causal effects and using them to guide allocation decisions with higher measurement precision without requiring the entire system to become excessively complex.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Ease of manufacture

If existing resource allocation techniques are used, then implementation is straightforward, but resource management outcomes are wasteful and costly due to inability to identify optimal allocations

Engineering Contradiction:

Improveease of implementationVSAvoidresource waste and cost

Core Design Contradiction:

Ease of manufactureVSLoss of energy

Solution Approach 1:

The patent implements feedback mechanisms where the system continuously learns from observed outcomes and adjusts its causal models accordingly. By monitoring the actual impact of resource allocations and comparing them with predicted outcomes, the system refines its causal inference models to better identify optimal allocations, reducing waste and improving resource management efficiency over time.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent performs preliminary causal effect estimation and optimization before actual resource allocation occurs. The system pre-computes causal relationships and identifies optimal allocation strategies based on learned patterns, allowing for more efficient resource distribution in advance rather than reacting to outcomes after the fact, thereby preventing resource waste before it occurs.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20240362068A1Predictive machine learning techniques for optimal resource allocation using nonlinear causal inference

Publication Date: 2024.10.31 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 generating an optimization function for generating an optimal amount of resources to allocate to data objects in each of one or more data object cohorts based on nonlinear causal effect predictions and generating an optimal parameter occurrence set based on the determined optimal amount of resource. Nonlinear causal effects of selected amounts of type-varied resources assigned to specific data objects are predicted on an outcome of interest associated with the data objects.