Slot Allocation Machine Learning Model for Resource Management

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Complexities arise in allocating resources to slots of a platform management matrix, leading to inefficiencies in ensuring support levels and equitable distribution, particularly in large-scale enterprise software applications where manual processes are prone to errors and downtime.

Innovation Solution

An apparatus utilizing a trained slot allocation machine learning model to allocate resources based on directive data structures and compliance matrices, generating adaptive directives for optimal slot allocation, thereby automating the process and reducing manual intervention.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual resource allocation processes are used, then flexibility in decision-making is maintained, but errors and downtime increase while productivity decreases

Engineering Contradiction:

Improveresource allocation efficiencyVSAvoidallocation accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent replaces manual resource allocation processes with an automated machine learning-based system. The ML model analyzes historical data, compliance matrices, and directive data structures to automatically generate slot allocation recommendations, eliminating human error and inefficiency while maintaining or improving flexibility through adaptive learning.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service resource allocation by allowing the machine learning model to autonomously analyze data, generate allocations, and provide recommendations without requiring manual intervention. The model continuously learns from feedback and compliance data, improving its allocation accuracy over time while reducing dependency on manual processes.

Inventive Principle:

Principle #25Self-service

2Productivity

If automated resource allocation is implemented, then productivity and consistency improve, but system complexity increases

Engineering Contradiction:

Improveallocation speedVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the resource allocation system into distinct modular components: directive data structures that define allocation rules, compliance matrices that track adherence, machine learning models that generate recommendations, and feedback mechanisms that provide compliance data. This modular architecture improves productivity while managing complexity through clear separation of concerns.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The machine learning model serves multiple functions: it analyzes historical allocation data, processes compliance matrices, generates slot allocation recommendations, and learns from feedback. This multi-functionality consolidates what would otherwise require multiple separate systems, improving productivity without proportionally increasing complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If machine learning models are used for slot allocation, then allocation optimization improves, but computational requirements and processing time increase

Engineering Contradiction:

Improveallocation optimizationVSAvoidmodel training and processing time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent performs preliminary actions by pre-training machine learning models on historical allocation data and compliance matrices before actual resource allocation is needed. The models are trained offline using directive data structures and historical performance data, so that during actual allocation, they can quickly generate recommendations without extensive real-time computation, reducing processing time while maintaining optimization.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements periodic retraining of machine learning models using updated compliance matrices and historical data. Instead of continuous heavy computation, the models are retrained at scheduled intervals with accumulated data, improving allocation optimization over time while avoiding constant computational overhead. This periodic action balances optimization quality with processing time requirements.

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS20250004835A1Apparatuses, methods, and computer program products for optimized support management slot allocations

Publication Date: 2025.01.02 ATLASSIAN PTY LTD

AI summary

An apparatus is configured to receive a first plurality of directive data structures, each directive data structure of the first plurality of directive data structures including a one or more first support control requirements associated with a supported platform and allocation of resources to a support matrix associated with the supported platform, the support matrix including a plurality of slots. The apparatus is further configured to allocate one or more resource data structures of a plurality of resource data structures to one or more of the plurality of slots of the support matrix according to the first plurality of directive data structures, and receive a slot allocation compliance matrix, where the slot allocation compliance matrix includes one or more compliance indications associated with the plurality of slots. The apparatus is further configured to apply a trained slot allocation machine learning model to the slot allocation compliance matrix and the support matrix to generate a second plurality of directive data structures.