Coverage Determination Using Biological Extraction and ML
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Solution Overview
Problem
Current coverage determinations lack factual evidence and fail to reward positive behaviors, and there is a lack of ability to utilize modifiable and non-modifiable information to generate effective coverage options.
Innovation Solution
A system and method that utilize a computing device to receive a coverage request, record user biological extraction, calculate effective age, determine behavior patterns, and identify danger profiles to produce a user coverage profile, which is used to select a machine-learning model to generate coverage options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional coverage determination methods are used, then the process is simple and quick, but the determination lacks factual evidence and accuracy
Solution Approach 1:
The system performs preliminary actions by collecting biological extractions, behavior patterns, and danger profiles before making coverage determinations. This advance data gathering and analysis enables accurate, evidence-based coverage decisions rather than relying on traditional simplistic methods.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw user data and coverage determinations. These models process complex biological and behavioral data to generate risk assessments that inform coverage decisions, acting as a mediator that transforms raw data into actionable insights.
2Adaptability or versatility
If traditional coverage methods are used, then the system is easy to operate, but positive behaviors are not rewarded and personalized coverage options cannot be generated
Solution Approach 1:
The system applies local quality by tailoring coverage options to individual users based on their specific biological extractions, behavior patterns, and danger profiles. Each user receives customized coverage recommendations rather than standardized offerings, enabling the system to reward positive behaviors specific to each individual.
Solution Approach 2:
The patent implements dynamics by allowing coverage options to adapt and change based on evolving user data. As users generate new behavioral data and biological information over time, the system dynamically updates risk assessments and adjusts coverage recommendations, enabling ongoing personalization.
3Reliability
If comprehensive user data is collected and analyzed, then accurate coverage options can be generated, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing biological extractions, behavior patterns, and danger profiles in structured formats. This advance preparation of data enables faster retrieval and analysis when generating coverage options, reducing processing time while maintaining reliability.
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with machine learning models that can efficiently analyze complex biological and behavioral data. These computational models process large datasets faster than conventional methods, reducing processing time while improving the reliability of coverage determinations.
Data Source
AI summary
A system for making a coverage determination. The system includes a computing device configured to receive from a remote device a coverage request. A computing device records a user biological extraction and utilizes the user biological extraction to calculate a user effective age. A computing device determines a user behavior pattern and identifies a user danger profile. A computing device produces a user coverage that is utilized in combination with a coverage machine-learning model to output a plurality of coverage options.