Machine-Learning Pipeline Orchestration for Adaptive Resource Allocation

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Solution Overview

Problem

Existing predictive data analysis processes face inefficiencies in resource allocation and processing time due to limitations in existing orchestration models, leading to suboptimal performance and waste in complex data processing tasks.

Innovation Solution

A machine-learning based orchestration model with multiple sub-models is trained using a feedback loop to optimize computing resources, allowing intelligent routing and placement of data objects through tailored processing techniques, enhancing efficiency and reducing waste.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional orchestration models are used for data processing, then system simplicity is maintained, but resource allocation efficiency deteriorates and processing time increases

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidorchestration model complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements feedback loops where the orchestration model continuously receives performance data from processed data objects and adjusts its routing decisions accordingly. This feedback mechanism enables the system to learn from past processing outcomes and optimize resource allocation dynamically, resolving the contradiction by making the complex model adaptive and self-improving rather than static

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The orchestration model performs self-optimization by automatically adjusting its own parameters and routing strategies based on accumulated processing experience. The system serves itself by generating training data from actual operations and retraining its internal models without external intervention, thereby managing complexity internally while delivering improved productivity

Inventive Principle:

Principle #25Self-service

2Reliability

If multiple predictive data analysis sub-routines are executed for all data objects, then processing thoroughness is improved, but resource waste increases

Engineering Contradiction:

Improveanalysis thoroughnessVSAvoidcomputing resource waste

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent applies different processing intensities to different data objects based on their local characteristics. The orchestration model analyzes specific attributes of each data object and routes only those requiring thorough analysis to intensive sub-routines, while applying lighter processing to others. This local differentiation maintains reliability for critical cases while eliminating unnecessary resource consumption elsewhere

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

Instead of applying full processing intensity uniformly to all data objects, the system applies partial action selectively. The orchestration model determines the appropriate level of processing needed for each data object, applying intensive analysis only when necessary to achieve reliable outcomes, thereby avoiding excessive resource consumption on cases that don't require such thorough treatment

Inventive Principle:

Principle #16Partial or excessive action

3Productivity

If computing resources are allocated uniformly across all data objects, then fairness is maintained, but processing performance deteriorates

Engineering Contradiction:

Improveoverall system performanceVSAvoidresource allocation fairness

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The patent transforms static uniform resource allocation into dynamic adaptive allocation. The orchestration model continuously monitors system state, data object characteristics, and processing outcomes, adjusting resource distribution in real-time based on current needs and performance feedback. This dynamic approach optimizes overall productivity while maintaining operational fairness through transparent, rule-based adaptation rather than arbitrary decisions

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20250321754A1Intelligent automatic orchestration of machine-learning based processing pipeline

Publication Date: 2025.10.16 OPTUM SERVICES IRELAND LTD

AI summary

Various embodiments of the present invention disclose techniques for orchestrating a complex data processing scheme for an investigative process using a machine-learning based orchestration model that is trained to optimize the use of computing resources based at least in part on a feedback loop. An input data object associated with an investigative process can be selected for investigation; a predictive data analysis sub-routine for processing the input data object can be intelligently selected from a plurality of predictive data analysis sub-routines by the machine-learning based orchestration model; and a processing orchestration action can be initiated based at least in part on an investigative score output by the predictive data analysis sub-routine. The processing orchestration action can include closing the input data object, continuing to process the input data object with additional predictive data analysis sub-routines, or passing the input data object to a predictive entity for further processing.