Population Data Optimization via Segmentation and Preliminary Action
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Solution Overview
Problem
Current optimization systems are inefficient in handling large populations exceeding 50,000 objects, take too long to produce results, and are cumbersome to use, lacking the ability to efficiently optimize output data in a short period while being user-friendly.
Innovation Solution
A system that optimizes output data by partitioning large populations into smaller, similar groups, using selection routines to optimize each partition, and generating output data through a computing device processor, allowing users to define optimization functions and constraints, and presenting results in a user-friendly manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current optimization systems are used to handle large populations, then optimization capability is provided, but processing efficiency deteriorates and time consumption increases
Solution Approach 1:
The patent divides the large population into multiple partitions or segments, allowing the optimization system to process smaller subsets independently and in parallel. This segmentation approach enables the system to handle large populations efficiently by breaking down the complex optimization problem into manageable chunks that can be processed faster and combined to produce the final optimized result.
2Measurement precision
If optimization systems process large populations, then comprehensive output data is generated, but computation time increases significantly
Solution Approach 1:
The patent performs preliminary actions by pre-processing the population data, identifying key characteristics and constraints before the main optimization process. This includes partitioning the population, pre-calculating relevant metrics, and setting up the optimization framework in advance, which reduces the computation time required during the actual optimization execution while maintaining output data accuracy.
3Adaptability or versatility
If optimization systems are designed for large-scale processing, then handling capacity increases, but system complexity increases
Solution Approach 1:
The patent employs segmentation to divide the complex optimization system into modular components that can independently handle different aspects of the optimization process. Each module processes specific partitions or aspects of the population, making the overall system more manageable and easier to implement while maintaining high handling capacity for large-scale data.
4Adaptability or versatility
If optimization systems handle increasing population sizes, then coverage improves, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service mechanisms where the optimization system automatically performs tasks such as population partitioning, parameter optimization, and result aggregation without requiring extensive user intervention. The system adapts to different population sizes and characteristics automatically, maintaining ease of operation while improving population coverage through automated scalability.
Data Source
AI summary
Embodiments of the present invention relate to systems, methods and computer program products for allowing a user to define output data to be optimized for a population, define one or more input constraints based on which the output data is to be optimized, and optimizing the output data for the population by selecting an object based on a selection routine. Embodiments of the present invention allow fast and accurate optimization of output data associated with large populations.