Decision Optimization Metamodel for Dynamic Data
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Solution Overview
Problem
Existing optimization platforms are inefficient in utilizing organizational resources for multi-record predictions and recommendations, as they assume static input data and fail to process dynamic data in real time, leading to underutilization of resources and inaccurate solutions.
Innovation Solution
A computer-implemented method and system that defines an optimization problem as a metamodel, incorporating both static and dynamic data, using an abstract syntax tree structure with a dynamic context to solve decision objectives and constraints, allowing for real-time optimization solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing optimization platforms use static data and mathematical techniques, then computational simplicity is maintained, but the ability to process dynamic data in real time deteriorates
Solution Approach 1:
The patent applies dynamics by transforming the optimization system from static to dynamic. The metamodel continuously adapts to changing input data in real-time, allowing the optimization problem definition, constraints, and objectives to evolve dynamically rather than remaining fixed. This enables the system to process dynamic data streams while maintaining computational efficiency through structured metamodeling.
Solution Approach 2:
The patent utilizes parameter changes by allowing input data parameters to vary over time while the metamodel structure remains consistent. The system handles changing data characteristics, constraints, and objectives by updating parameter values within the established metamodel framework, enabling real-time adaptation without complete system reconfiguration.
2Productivity
If existing platforms focus on singular record predictions, then computational resources are conserved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent merges multiple singular record optimizations into a unified multi-record optimization framework. By aggregating records and applying optimization at the organizational level rather than individual record level, the system achieves better resource utilization. The metamodel consolidates multiple objectives and constraints into a single optimization problem, reducing redundant computations and improving overall efficiency.
Solution Approach 2:
The patent implements universality by creating a metamodel that handles multiple record types, objectives, and constraints through a single unified framework. This multi-functional approach allows the same optimization infrastructure to serve various business scenarios and data types, maximizing resource utilization across different use cases rather than requiring separate systems for each scenario.
3Measurement precision
If existing optimization systems use simplified static assumptions, then solution speed is improved, but solution accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-defining the metamodel structure, constraints, and objective functions before actual optimization execution. This preparatory work establishes a robust framework that can quickly process dynamic data while maintaining accuracy. The metamodel template is prepared in advance with all necessary components, enabling rapid solution generation when real-time data becomes available without sacrificing precision.
Data Source
AI summary
A computer-implemented method of decision optimization in a multi-record environment is disclosed. The method includes receiving a request to make a recommendation in relation to a data record and defining the recommendation in terms of an optimization problem including decision objectives including objective contribution functions and constraints including constraint contribution functions. The method also includes extracting input data from a data source, the input data including individual instances of data and attributes describing the individual instances of data. The method also includes identifying a context of the optimization problem based upon the individual instances of data. The context relates to a behavior of the input data given the decision objectives and the constraints. The method further includes solving the optimization problem by satisfying the decision objectives and the constraints, in the context, to generate a solution and providing the recommendation based on the solution.