Multi-Tier Resource Orchestration via Computational Model Adaptation
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Solution Overview
Problem
Existing load handling systems face limitations in resource availability due to varied quantities, capacities, and capabilities, leading to sub-optimal performance and results.
Innovation Solution
A system and method for multi-tier resource network adaptation and orchestration, which includes collecting data on resource assignments, interactions, and load conditions, processing this data to identify and map resources, creating a network of content nodes, and automatically training a computational model to optimize resource performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resources are allocated based on fixed capacities and capabilities, then resource assignment is simple and manageable, but resource availability and process performance are limited and sub-optimal
Solution Approach 1:
The patent implements dynamic resource orchestration where the system continuously adapts resource allocation based on changing process requirements and resource availability. The computational model learns from operational data to dynamically adjust resource assignments, transforming static resource management into a dynamic, self-optimizing system that resolves the contradiction between simple management and optimal performance.
Solution Approach 2:
The system employs self-service mechanisms through automated computational models that independently analyze resource performance data and make optimization decisions without manual intervention. The model automatically trains on collected data and generates optimized resource assignments, enabling the system to self-improve while maintaining simplicity for users.
2Productivity
If resource quantities and capacities are increased to handle higher loads, then process performance improves, but resource availability and utilization efficiency are reduced
Solution Approach 1:
The patent changes the parameters of resource utilization by using a computational model that analyzes multiple resource attributes simultaneously (speed, efficiency, accuracy, reliability) rather than relying on single metrics. This multi-parameter optimization enables the system to achieve higher load handling capacity by optimally combining existing resources with varying characteristics, avoiding the need to increase resource quantities.
Solution Approach 2:
The system creates a universal resource orchestration framework that can allocate any process-performing resource to any task based on real-time compatibility analysis. The computational model evaluates multiple resource types and their various capabilities, enabling existing resources to be used more universally and efficiently across different processes and load conditions, thereby increasing effective capacity without adding resources.
3Reliability
If specialized resources are used to improve process performance, then accuracy and reliability improve, but resource availability and flexibility are reduced
Solution Approach 1:
The patent implements feedback mechanisms where the computational model continuously monitors resource performance data including reliability metrics and accuracy measurements. This feedback loop enables the system to learn which specialized resources perform best for specific processes and dynamically adjust allocations to maintain high reliability while adapting to changing requirements. The model uses this feedback to balance specialization with adaptability.
Solution Approach 2:
The system performs preliminary analysis of resource capabilities and process requirements using the trained computational model before making allocations. By pre-evaluating compatibility between specialized resources and process needs, the system can proactively assign the right specialized resources to appropriate tasks, ensuring high reliability while maintaining the flexibility to adapt allocations as conditions change. This preliminary matching reduces the need for rigid specialized assignments.
Data Source
AI summary
Systems, methods, and machine-readable media to orchestrate process-performing resources are disclosed. Collected data items may correspond to assignments of process-performing resources, and device interactions or data changes that correspond to process or operation performances, conditions of loads. Data items may be processed to identify and map data portions to process-performing resources. Content nodes may be created and linked in a network of content nodes configured according to a computational model that comprises hierarchical orderings of the content nodes using a network data structure. A graphical representation may be formatted to represent the network data structure of the content nodes linked in the network. Metrics of resource performance corresponding to content nodes may be determined. The computational model may be trained using the metrics to create an adapted computational model. Adapted content nodes may be created according to the adapted computational model.