Precompiled Data Processing Models for Rapid Parameter Calculation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional data processing models, such as Rate Order Calculators (ROCs), are bespoke, time and resource-intensive, and do not scale well to adapt to different use cases or frequent parameter changes, leading to inefficient and slow calculation of output values.
Innovation Solution
A data processing model builder precompiles models using a graph structure, allowing operators to input parameters intuitively without coding, enabling rapid deployment and instantaneous calculation of personalized parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional brute force computation is used for complicated calculations with interdependent parameters, then calculation accuracy is maintained, but calculation time becomes unacceptably long and results are not useful in real-time scenarios
Solution Approach 1:
The patent applies preliminary action by precompiling data processing models before runtime. The system receives parameters, builds a graph representing dependencies between parameters and attributes, compiles the data processing model using these parameters and graph, and stores the compiled model for rapid execution. This precompilation phase performs the complex computational work in advance, allowing the runtime system to execute precompiled models quickly without performing heavy calculations during actual use, thus resolving the contradiction between maintaining calculation accuracy and reducing calculation time.
2Measurement precision
If bespoke Rate Order Calculators are developed for specific products with complex interrelated equations, then calculation accuracy for that specific use case is achieved, but development time and resource requirements increase significantly and the system does not scale to multiple products
Solution Approach 1:
The patent implements universality by creating a generic data processing model compilation system that can handle multiple products and use cases through a unified framework. Instead of developing separate bespoke calculators for each product, the system uses a common compilation process that receives parameters, builds dependency graphs, and compiles models that can be applied across different products. This universal approach allows the same infrastructure to serve multiple products with different parameter sets, significantly improving development efficiency and scalability while maintaining calculation accuracy for each specific use case.
Solution Approach 2:
The patent applies parameter changes by allowing the data processing model to adapt to different products through parameter configuration rather than code modification. The system receives parameters that define the specific calculations needed for different products, builds dependency graphs based on these parameters, and compiles models dynamically. This parameter-driven approach enables the same compilation framework to handle diverse product requirements by simply changing the input parameters, eliminating the need to develop separate bespoke calculators for each product while maintaining accurate calculations for each specific use case.
3Adaptability or versatility
If conventional data processing models are adapted to different use cases or parameter changes, then versatility is improved, but significant time and effort are required for each adaptation
Solution Approach 1:
The patent applies preliminary action by performing the adaptation work during the precompilation phase rather than at runtime. The system receives parameters defining the specific use case, builds a dependency graph representing the relationships between parameters and attributes, and compiles the data processing model in advance. This preliminary compilation captures all the adaptation logic beforehand, allowing the compiled model to be executed quickly for the specific use case without requiring time-consuming adaptations at runtime, thus resolving the contradiction between versatility and adaptation time.
Solution Approach 2:
The patent implements parameter changes as the core mechanism for adaptation. Instead of modifying code or structure to adapt to different use cases, the system changes the input parameters that define the data processing model. The compilation process takes these parameters and automatically generates the appropriate dependency graphs and compiled models. This parameter-driven adaptation approach enables rapid switching between different use cases by simply providing different parameter sets, eliminating the need for time-consuming manual adaptations while maintaining high versatility.
Data Source
AI summary
A data processing model builder precompiles a data processing model using a dependency tree and parameters provided by a provider of a product or service. New parameters can be provided in an intuitive way, without the need to understand computer program code, and a new data processing model can be compiled quickly and efficiently. The precompiled data processing model can then be deployed to provide personalized parameters for the product or service to end users almost instantaneously on request.