Mission Plan Data Structure for Military Resource Allocation

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Solution Overview

Problem

Computing an efficient allocation of resources for military mission plans is challenging due to the complexity of coordinating tasks with available assets, including their capabilities and geographic locations.

Innovation Solution

A computer architecture that utilizes a graphical user interface to input tasks and assets, with processing circuitry generating a mission plan data structure by assigning available assets to tasks based on asset type, capabilities, and geographic location, using machine-learning algorithms for efficient resource allocation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual resource allocation methods are used for military mission plans, then flexibility and adaptability are maintained, but computing efficiency and allocation optimization are insufficient

Engineering Contradiction:

Improvecomputing efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical resource allocation with an automated computer-implemented system that uses algorithms and data structures to compute optimal mission plans, substituting human cognitive processes with computational processes to achieve higher efficiency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by automatically generating mission plans and allocating resources without requiring manual intervention, using the input data to compute solutions autonomously through programmed algorithms

Inventive Principle:

Principle #25Self-service

2Measurement precision

If comprehensive asset capabilities and geographic locations are considered in resource allocation, then allocation accuracy is improved, but computational complexity increases

Engineering Contradiction:

Improveallocation accuracyVSAvoidcomputational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the complex resource allocation problem into distinct components by using separate data structures for tasks and assets, allowing the system to process and evaluate each component independently while maintaining overall allocation accuracy

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system changes parameters by representing assets and tasks with specific data structures that capture essential attributes (capabilities, locations, requirements), enabling accurate comparison and matching without processing all possible variables

Inventive Principle:

Principle #35Parameter changes

3Productivity

If machine-learning algorithms are used for resource allocation, then optimization quality is improved, but processing time and computational resources increase

Engineering Contradiction:

Improveoptimization qualityVSAvoidprocessing time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent applies partial action by implementing machine-learning algorithms selectively for the most critical aspects of resource allocation rather than processing every possible optimization scenario, achieving good enough solutions more quickly

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system performs preliminary action by pre-processing and structuring asset and task data into standardized data structures before applying machine-learning algorithms, reducing the computational burden during the actual optimization process

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10877634B1Computer architecture for resource allocation for course of action activities

Publication Date: 2020.12.29 RAYTHEON CO

AI summary

A computing machine receives an input comprising: a representation of one or more tasks and a representation of one or more assets. The computing machine initiates generation of a mission plan data structure comprising an assignment of an available asset to each task. Upon successfully generating the mission plan data structure, the computing machine provides an output comprising the mission plan data structure.