Dynamic Objective Function Configuration for Data Optimization
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Solution Overview
Problem
Existing objective functions in software programs are static and fail to adapt to changing organizational priorities, limiting their flexibility and applicability across different organizations with varying interests.
Innovation Solution
A multi-objective optimization service that dynamically configures parameter values and constraints of an objective function template at runtime, allowing organizations to tailor the optimization process to their specific interests through user input, enabling flexible adaptation to changing priorities and interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an objective function is statically defined based on a selection criteria, then the selection process is simple and reliable, but the objective function cannot adapt to changing organizational priorities over time
Solution Approach 1:
The patent transforms the static objective function into a dynamic one by introducing configurable parameters that can be adjusted at runtime. The objective function becomes adaptable to changing organizational priorities while maintaining its core selection logic, resolving the contradiction between reliability and adaptability.
Solution Approach 2:
The patent enables modification of parameter values within the objective function to reflect changing organizational interests. By allowing parameter reconfiguration without reconstructing the entire objective function, the system maintains reliability while gaining adaptability to new priorities.
2Adaptability or versatility
If a new objective function is constructed when priorities change, then the objective function accurately represents current interests, but the process is time-consuming and complex
Solution Approach 1:
The patent pre-structures the objective function with configurable parameters that anticipate future priority changes. This preliminary setup allows rapid adaptation when priorities change, eliminating the need for time-consuming reconstruction while maintaining accuracy to current interests.
Solution Approach 2:
The dynamic parameter configuration enables the objective function to adapt to changing priorities in real-time without reconstruction. This dynamic capability resolves the contradiction between accuracy to current interests and the time required to update the objective function.
3Reliability
If an objective function is tailored for use by a particular organization, then it accurately represents that organization's interests, but it is not beneficial for another organization unless both have the exact same interests
Solution Approach 1:
The patent creates a universal objective function template with configurable parameters that can be adapted to different organizations' interests. The same core function serves multiple organizations by adjusting parameter values, resolving the contradiction between reliability for a specific organization and versatility across organizations.
Solution Approach 2:
By allowing parameter customization, the patent enables a single objective function structure to serve multiple organizations with different interests. The parameter changes allow each organization to tailor the function to their specific needs while maintaining the same underlying algorithm, achieving both reliability and versatility.
4Adaptability or versatility
If the objective function is reconfigured dynamically, then it adapts to changing priorities and interests, but the system complexity increases
Solution Approach 1:
The patent segments the objective function into a stable core structure and configurable parameter components. This segmentation allows dynamic reconfiguration of parameters without affecting the core logic, achieving flexibility while managing system complexity through modular design.
Solution Approach 2:
The dynamic parameter configuration system provides flexibility to changing priorities while maintaining a stable core objective function structure. This dynamic approach manages complexity by separating the invariant core logic from the variable parameters that need adaptation.
Data Source
AI summary
Provided is a system and method for dynamic configuration of a multi-objective optimization function and identifying an optimal set of records based thereon. In one example, the method may include receiving a set of data records and priority values to be applied to the set of data records, generating an objective function from an objective function template stored in a memory device, wherein the generating comprises dynamically configuring parameter values of the objective function based on the priority values, executing the objective function on the set of data records and identifying an optimal subset of data records from among the set of data records based on the dynamically configured parameter values of the executing objective function, and displaying identifiers of the identified optimal subset of data records.