Dynamic Process Orchestration with Rule-Based Adaptation
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Solution Overview
Problem
Enterprise organizations face inefficiencies due to static computerized processes that fail to adapt to changing business rules and market conditions, leading to increased costs, longer time to market, and inconsistent customer treatment, as well as difficulties in aggregating meaningful data for decision-making processes.
Innovation Solution
A process orchestration and dynamic data acquisition system that allows for flexible process flows driven by updatable business rules, utilizing reusable processes and dynamic data aggregation from both internal and external sources to adapt to customer and product requirements, reducing programmer involvement and leveraging only necessary data for each request.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If static process flows are used, then process stability is maintained, but adaptability to changing business rules deteriorates
Solution Approach 1:
The patent implements dynamic process flows that can be modified at runtime based on changing business rules. The system uses a process engine that loads process definitions from external sources (XML, JSON, etc.) allowing processes to be updated without system reconfiguration. This enables the process flow to adapt dynamically to new business requirements while maintaining operational stability through controlled update mechanisms.
Solution Approach 2:
The system allows business rules to be changed as parameters in the process flow without modifying the underlying process structure. By separating process logic from business parameters, the system can adjust parameters (such as approval thresholds, routing rules, data requirements) to reflect changing business conditions while maintaining the stable process framework.
2Adaptability or versatility
If static processes are modified to adapt to changes, then adaptability improves, but implementation time and cost increase
Solution Approach 1:
The patent segments the process system into independent, reusable components (process definitions, business rules, data sources, activities). Each segment can be modified independently through configuration files or databases without affecting the entire system. This modular approach allows rapid adaptation by simply updating the relevant segment rather than reimplementing the whole process.
Solution Approach 2:
The system performs preliminary action by pre-defining process templates and business rule frameworks that can be quickly instantiated and customized. Rather than building processes from scratch when changes are needed, the system has pre-configurable elements that can be activated or modified through simple configuration updates, dramatically reducing implementation time.
3Reliability
If all process steps are performed step-wise, then completeness is ensured, but efficiency deteriorates when not all steps are required
Solution Approach 1:
The patent implements conditional process execution where only the necessary steps are performed based on the specific situation. Business rules evaluate process data and dynamically determine which activities must be executed, allowing the system to perform partial action (only required steps) rather than forcing completion of all steps. This maintains reliability for required steps while improving efficiency by skipping unnecessary ones.
Solution Approach 2:
The system uses feedback mechanisms where business rules continuously evaluate process state and data, then dynamically adjust the process flow. This feedback loop ensures that completeness is maintained for required steps while enabling early termination or skipping of steps that are not needed, optimizing the balance between reliability and efficiency.
4Loss of information
If data aggregation from multiple sources is performed, then data completeness improves, but system complexity increases
Solution Approach 1:
The patent implements a universal data access layer that handles multiple data sources (databases, APIs, files, external systems) through a single unified interface. This multi-functional component abstracts the complexity of different data sources, allowing the system to aggregate data from numerous sources without proportionally increasing overall system complexity. The universal adapter pattern enables consistent data retrieval regardless of source type.
Solution Approach 2:
The system introduces intermediary components (data adapters, integration layers, ESB) that mediate between the process engine and diverse data sources. These intermediaries handle the complexity of data aggregation, transformation, and validation, shielding the core process logic from source-specific complexities while ensuring complete data collection from all required sources.
Data Source
AI summary
A process orchestration and dynamic data acquisition system may allow process flows to be flexible and to customer or product requirements based on rules obtained from a rule management system. The process orchestrator may be driven based on an updatable and dynamic set of business rules to allow for quick changes to the process flow. Through the business rules the process orchestrator retrieves and processes data necessary to the process based on a customer request and/or product or service provided. The process orchestrator provides dynamic data aggregation from both internal and external data sources, through calling re-useable processes instantiated by the process host system and continuously adapts the process flow to meet the unique needs of each request input and results returned from each dynamic process call.