Business Policy Analysis via Segmented Rule Framework
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Solution Overview
Problem
Current business rule management systems are inadequate for dynamic environments, as they require significant time and resources to modify policies, and traditional data analysis methods are limited in their ability to analyze business rules effectively, often relying on historical data and being unsuitable for remote access and real-time adjustments.
Innovation Solution
A system and method that allows users to analyze the impact of adjusting business rule parameters on business metrics using simulated, historical, or hybrid data, enabling the selection of optimal parameter values to meet business objectives, while eliminating dependence on historical data and supporting real-time and futuristic data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software programs are used to encode business policies, then automation efficiency and elimination of human errors are improved, but the time and resources required to modify policies when they change increase significantly
Solution Approach 1:
The patent segments the business policy into two distinct layers: a static structural framework and dynamic business rules. The framework remains unchanged while only the rules need modification, allowing policy updates without restructuring the entire system. This segmentation enables efficient updates by isolating the mutable rule component from the stable framework.
Solution Approach 2:
The patent introduces dynamic business rules that can be modified independently of the static policy framework. The rules are designed to be adaptable and changeable without requiring modifications to the underlying software structure, enabling the system to respond dynamically to changing business requirements while maintaining automation efficiency.
2Loss of information
If traditional data analysis methods are used, then analysis can be performed, but dependence on historical data limits the ability to analyze futuristic or hypothetical scenarios
Solution Approach 1:
The patent creates simulated data that copies the structure and characteristics of historical data but represents hypothetical or futuristic scenarios. This simulated data can be used to analyze policy impacts without relying on actual historical records, enabling analysis of scenarios that have not yet occurred or may never occur in reality.
Solution Approach 2:
The patent enables analysis by changing the temporal parameter of the data from historical to simulated/futuristic. By varying this parameter, the system can switch between analyzing past performance and evaluating future scenarios, providing versatility in data source selection while maintaining consistent analysis methodologies.
3Reliability
If comprehensive policy analysis is performed to optimize business metrics, then policy effectiveness is improved, but the resources and complexity required for analysis increase
Solution Approach 1:
The patent segments the analysis process to focus only on the impact of rule parameter changes rather than reanalyzing the entire policy framework. By isolating the specific rules and parameters that need adjustment, the system reduces analysis complexity while maintaining comprehensive evaluation of policy effectiveness through targeted simulation of metric impacts.
Solution Approach 2:
The patent performs preliminary simulation of policy changes before actual implementation. By pre-analyzing the impact of potential rule modifications on business metrics using simulated data, the system identifies optimal changes without requiring complex real-time analysis, reducing the complexity of the deployment process while ensuring policy effectiveness.
Data Source
AI summary
Methods and systems are provided for analyzing the business rules, business metrics, and decision parameters for a firm or organization, processing a subset of such data to form output, and offering access to selective views of such output including evaluation and comparative data regarding execution of such business rules, information on corresponding business metrics or sets of business metrics, information on corresponding decision parameters or sets of decision parameters or scenarios, and other useful analytic information which can help a firm or organization evaluate and modify business policies based on said rules, metrics, and parameters. In addition to said rules, metrics and/or parameters, the data for the business rule analysis can include conventional historical data or hypothetical data based on simulations which the current system and method provide based on prescribed random and non-random algorithms. The simulated or hypothetical data enables users to conduct rule analysis based on historical data, simulated data, or hybrid models. In this manner, the methods and systems described in this invention provide for both an evaluation of a firm's current policies as well as an evaluation of policy modifications not actually executed but for which hypothetical data can be provided and analyzed.