Predictive Target Event Evaluation Using Valuation Distributions
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Solution Overview
Problem
Current methods for predictive target event evaluation are inefficient and lack computational efficiency, making it challenging to perform accurate predictive analytics in fields like drug pricing and savings opportunities.
Innovation Solution
The implementation of a system that uses a valuation distribution data object to determine proposed event alternatives by identifying target events, calculating granularity-adjusted event feature combinations, and estimating utility measures based on segment valuation distributions, thereby improving computational efficiency with linear complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional predictive target event evaluation methods are used, then comprehensive event analysis can be performed, but computational efficiency is poor and processing time is excessive
Solution Approach 1:
The patent segments the valuation evaluation process into distinct components: (1) determining event alternatives based on target event features, (2) retrieving valuation distributions from pre-computed data structures, (3) calculating utility measures using standardized formulas. This segmentation allows each component to be optimized independently and enables parallel processing of multiple event alternatives, significantly improving computational efficiency while reducing processing time.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing valuation distributions in data structures before actual predictive evaluation is needed. These pre-computed valuation distributions contain aggregated historical data that can be quickly retrieved during event evaluation without requiring real-time computation, thereby reducing processing time while maintaining comprehensive analysis capabilities.
2Measurement precision
If detailed event feature analysis is performed for each proposed event alternative, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent changes parameters by using standardized utility measure formulas that transform detailed event features into comparable quantitative values. Instead of performing complex qualitative analysis on each event feature, the system converts features into numerical parameters that can be efficiently processed through mathematical formulas, maintaining prediction accuracy while reducing computational complexity.
Solution Approach 2:
The patent uses copying by retrieving pre-computed valuation distributions from data structures rather than重新computing them for each event alternative. These copied valuation distributions contain aggregated patterns from historical data that can be applied to multiple event scenarios, reducing computational complexity while preserving the accuracy benefits of detailed analysis.
3Reliability
If comprehensive valuation distribution data is maintained for all event scenarios, then prediction reliability is improved, but data storage requirements and processing overhead increase
Solution Approach 1:
The patent merges similar valuation patterns by aggregating historical event data into consolidated valuation distributions grouped by event feature categories. Instead of maintaining separate data for every possible event scenario, the system merges comparable events into unified distributions that can be applied to multiple scenarios, reducing data storage requirements while maintaining prediction reliability through pattern recognition.
Solution Approach 2:
The patent creates universal valuation distribution data structures that serve multiple event evaluation purposes. These pre-computed distributions can be reused across different event alternatives and prediction scenarios, reducing the total data storage requirements while ensuring consistent and reliable predictions across various contexts through multi-functional data utilization.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for predictive target event evaluation. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive target event evaluation using at least one of a valuation distribution data object, historical event data, and a granularity-adjusted event feature combination.