Dynamic Goal Optimization for Conversational Automation
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Solution Overview
Problem
Current conversational process automation systems lack the ability to recommend next best actions that align with organizational goals at an aggregate level, failing to dynamically adjust to changing business objectives such as improving Net Promotor Score, reducing fraud leakage, or optimizing process efficiency.
Innovation Solution
A computer-implemented method that enriches received information using natural language processing and machine learning to dynamically generate recommendations that satisfy comprehensive organizational goals, allowing for customizable action recommendations that can be executed to achieve specific KPIs without requiring retraining of the entire system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional workflow automation tools are used with fixed action lists, then system simplicity is maintained, but the system cannot dynamically adapt to changing organizational goals
Solution Approach 1:
The system dynamically generates action lists based on current organizational goals and KPIs rather than using fixed predetermined lists. The recommendation engine continuously adapts recommendations based on changing goals, ensuring the system remains versatile while maintaining manageable complexity through automated goal-based generation.
Solution Approach 2:
The system changes parameters by adjusting which actions are recommended based on weighted KPIs and organizational goals. By modifying the priority weights of different KPIs dynamically, the system adapts to changing organizational objectives without requiring complete system redesign.
2Measurement precision
If the system processes all historical data instances to ensure goal satisfaction, then recommendation accuracy improves, but processing time increases
Solution Approach 1:
The system extracts and processes only the most relevant features and attributes from historical data instances rather than processing complete data sets. By selecting key attributes that directly impact goal satisfaction, the system maintains recommendation accuracy while significantly reducing processing time.
Solution Approach 2:
The system processes a subset of historical data instances that are most relevant to current goals rather than exhaustively processing all available data. This partial processing approach achieves sufficient recommendation accuracy without the time cost of complete data analysis.
3Adaptability or versatility
If multiple KPIs are tracked and optimized simultaneously, then comprehensive goal coverage is achieved, but system complexity increases
Solution Approach 1:
The recommendation engine serves multiple KPIs and organizational goals simultaneously through a single unified system. By designing the system to handle diverse KPIs (customer satisfaction, fraud detection, efficiency metrics) through a common recommendation framework, the system achieves comprehensive goal coverage without proportionally increasing complexity.
Data Source
AI summary
Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention enrich received information based on identified attributes. Embodiments of the present invention can then dynamically generate a recommendation that satisfies a goal based, at least in part on the enriched information. Embodiments of the present invention can then execute at least one dynamically generated goal that satisfies the goal.