Dynamic Sub-Target Identification for Real-Time Objective Optimization
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Solution Overview
Problem
Optimizing objective functions related to goal-oriented processes is labor-intensive and time-consuming due to the large number of variables that need to be evaluated, especially with constraints of static goals and real-time data and feedback, necessitating a more agile and responsive approach.
Innovation Solution
An apparatus and method for identifying dynamic sub-targets using a processor to receive entity data, identify static targets through a first objective function, and iteratively determine dynamic sub-targets based on product data, generating a target report.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optimization methods are used to optimize objective functions with multiple variables, then optimization accuracy can be maintained, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent segments the complex optimization problem into hierarchical levels: macro-level static targets and micro-level dynamic sub-targets. This segmentation allows the system to process optimization in manageable chunks rather than evaluating all variables simultaneously, reducing computational time while maintaining accuracy through iterative refinement at each level.
Solution Approach 2:
The patent introduces dynamic sub-targets that adapt in real-time based on feedback from the objective function evaluation. Unlike static targets, these dynamic sub-targets adjust their parameters iteratively, allowing the optimization process to respond to changing conditions and converge faster on optimal solutions without sacrificing precision.
2Stability of the object's composition
If static goals are used to constrain optimization, then goal clarity is maintained, but the process becomes less agile and responsive to real-time data
Solution Approach 1:
The patent implements a dual-target system where static goals provide stable directional guidance while dynamic sub-targets adapt to real-time feedback. The dynamic sub-targets continuously adjust their parameters based on objective function evaluations, enabling the system to maintain goal stability at the macro level while being highly responsive to changing conditions at the micro level.
Solution Approach 2:
The patent incorporates real-time feedback loops where the results of objective function optimization are fed back to adjust dynamic sub-targets. This feedback mechanism allows the system to learn from each iteration and adapt its targets accordingly, maintaining both stability through the static goals and versatility through the adaptive dynamic sub-targets.
3Reliability
If all variables are evaluated simultaneously to optimize the objective function, then comprehensive optimization is achieved, but the complexity of the process increases significantly
Solution Approach 1:
The patent divides the variable evaluation process into hierarchical segments: static targets define broad optimization directions while dynamic sub-targets handle specific variable adjustments. This segmentation transforms a single complex simultaneous evaluation into multiple simpler sequential evaluations, maintaining comprehensive optimization coverage while reducing process complexity at each stage.
Solution Approach 2:
The patent employs dynamic sub-targets that evolve through iterative optimization cycles. Rather than evaluating all variables simultaneously with fixed targets, the system dynamically adjusts sub-target parameters based on previous iteration results, simplifying each evaluation step while ensuring comprehensive optimization through cumulative learning across iterations.
Data Source
AI summary
An apparatus for the identification of dynamic sub-targets is disclosed. The apparatus includes a processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a plurality of entity data comprising a plurality of product data. The processor identifies one or more static targets as a function of the plurality of entity data. The processor identifies a first set of dynamic sub-targets as a function of the one or more static targets and the plurality of product data. The memory instructs the processor to iteratively determine a static target status as a function of the first set of dynamic sub-targets and the one or more static targets. The processor identifies a second set of dynamic sub-targets as a function of the static target status. The processor generates a target report as a function of the static target status and the second set of dynamic sub-targets.