Rule Processing Engine for Retail Data Elements
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Solution Overview
Problem
Existing systems face challenges in efficiently applying rules to large numbers of data elements with varying structures, requiring users to define multiple rules and incurring significant processing overhead.
Innovation Solution
A system comprising a memory queue and a rule processing engine with a condition processing engine and an action processing engine, allowing rules to be applied independently of data element structures, reducing processing overhead by storing and processing data elements that satisfy conditions in a streamlined manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a user defines multiple rules to account for different data element structures, then the system can handle diverse data structures, but the processing time and complexity increase significantly
Solution Approach 1:
The patent applies universality by creating a single rule definition format that can handle multiple data element structures. The rule processing engine uses a unified approach to process rules across different data types, eliminating the need for users to define separate rules for each structure. This is achieved through a standardized condition evaluation mechanism that works universally across diverse data elements.
Solution Approach 2:
The patent segments the rule processing into distinct phases: condition evaluation phase and action execution phase. The condition processing engine separates the evaluation of rule conditions from the execution of rule actions, allowing for more efficient processing. This segmentation enables the system to pre-evaluate conditions and then execute actions in a streamlined manner, reducing overall processing time.
2Productivity
If the system processes rules for each data element structure individually, then processing can be targeted and efficient, but the processing overhead increases due to repetitive preparation and action execution
Solution Approach 1:
The patent applies preliminary action by having the condition processing engine evaluate and prepare rule conditions before executing the actual rule actions. The system pre-processes condition evaluations and stores intermediate results, so that when rule actions need to be executed, the groundwork is already laid. This reduces the processing overhead during action execution and improves overall productivity.
Solution Approach 2:
The patent merges the processing of multiple data element structures into a unified rule processing framework. Instead of handling each structure separately with its own processing overhead, the system combines all structures into a single processing pipeline that applies the same condition evaluation and action execution mechanisms. This consolidation eliminates repetitive preparation overhead while maintaining targeted processing capability.
3Ease of operation
If the system stores all data elements in memory for processing, then rule application becomes simpler, but the memory requirements and processing load increase
Solution Approach 1:
The patent extracts only the necessary data elements that satisfy the rule conditions into a separate processing queue. Instead of storing all data elements in memory, the system filters and extracts only those that meet the specified conditions for further processing. This extraction approach reduces memory usage while maintaining the simplicity of rule application, as the system only processes the extracted subset of data elements that are relevant to the rule.
Data Source
AI summary
A system for processing rules on data elements is described. The system includes a memory queue configured to receive and store a listing of data elements and a rule processing engine configured to apply one or more processing rules to one or more data elements. The rules processing engine further includes a rule condition processing engine configured to apply a condition to a data element and store the data element in the memory queue if the condition is satisfied and a rule action processing engine configured to iteratively perform an action on the elements stored in the memory queue.


