Prospective Prioritization Across Predictive Input Channels
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Solution Overview
Problem
Current data prioritization methods across predictive input channels are inefficient and unreliable, leading to suboptimal data retrieval and storage efficiency, as they lack a comprehensive approach to combine and prioritize data from multiple channels effectively.
Innovation Solution
The implementation of a method that determines predictive input channels for each entity, using model-based and rule-based evaluation techniques, to generate prospective prioritization scores based on triggering events, qualifying criteria satisfaction, and cost predictions, enabling the aggregation and prioritization of data across channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from multiple predictive input channels are processed separately using traditional methods, then each channel can maintain its own evaluation standards, but data retrieval efficiency decreases and storage needs increase
Solution Approach 1:
The patent combines data from multiple predictive input channels (model-based prospective, rule-based prospective, model-based real-time, and rule-based real-time channels) into a unified prioritization framework. By merging these channels and applying consistent prioritization logic across all of them, the system achieves both reliable data evaluation and improved retrieval efficiency, resolving the contradiction between maintaining channel-specific standards and achieving overall system efficiency.
2Reliability
If comprehensive data aggregation across all predictive input channels is performed, then data retrieval reliability improves, but storage requirements increase
Solution Approach 1:
The patent extracts only the essential prioritization information from each predictive input channel rather than storing complete datasets. By determining priority scores based on triggering events, qualifying criteria satisfaction, and cost predictions from each channel, then consolidating only these prioritized results, the system achieves reliable data retrieval without proportionally increasing storage requirements.
3Measurement precision
If multiple evaluation techniques (model-based and rule-based) are applied across all channels, then prioritization accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the evaluation system into distinct predictive input channels, each handling specific evaluation techniques (model-based or rule-based). This segmentation allows each channel to maintain specialized evaluation logic while the overall system manages complexity through modular architecture. The prioritization process then integrates results from these segmented channels without requiring all techniques to operate simultaneously in a monolithic system.
Data Source
AI summary
There is a need for more effective and efficient data prioritization with respect to predictive input entities across predictive input channels. This need can be addressed by, for example, techniques for prospective prioritization that utilize supervised machine learning models. In one example, a method includes determining a prospective priority score for each predictive input entity of a group of predictive input entities based on a predictive input channel for the predictive input entity and performing prospective prioritization of the group of predictive input entities based on each prospective priority score for a predictive input entity.