Duration-Dependent Hybrid Generation for Resource Planning
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Solution Overview
Problem
Current methods for planning resource cessation, such as the 4% rule, are not scientifically tailored to meet individual goals and are prone to pitfalls like living beyond 30 years or market variability, and blind investment strategies may not effectively manage longevity risk.
Innovation Solution
A computing system that provides duration-dependent hybrid generation, taking into account client demographics, risk tolerance, and target cessation parameters to offer customized investment and annuity allocations, using stochastic calculus and machine-learning models to optimize resource management and reduce reliance on conventional computational methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computational methods (e.g., 4% rule) are used for resource cessation planning, then the planning process is simple and easy to implement, but the precision and reliability of meeting individual client goals deteriorates
Solution Approach 1:
The patent replaces conventional mechanical computational methods (spreadsheets, 4% rule calculations) with machine learning models that automatically process client data and generate resource cessation plans. The ML models are trained on historical data to predict optimal resource allocation strategies, substituting manual calculation systems with intelligent automated systems that provide both precision and user-friendly interfaces.
Solution Approach 2:
The patent introduces an intermediary layer between client input and resource cessation recommendations by using trained machine learning models as mediators. These models process raw client data (demographics, risk tolerance, goals) through learned patterns and relationships, transforming simple inputs into precise, personalized planning recommendations without requiring complex user-side computations.
2Reliability
If duration-dependent hybrid generation with machine-learning models is implemented, then the reliability and precision of resource planning improves, but the device complexity and computational requirements worsen
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive historical data before deployment. The models are trained offline using duration-dependent hybrid generation techniques to learn optimal resource cessation strategies across various scenarios. This preliminary training phase captures complex relationships and patterns, so that during actual client interactions, the pre-trained models can rapidly provide reliable recommendations without requiring complex real-time computations.
Solution Approach 2:
The patent utilizes parameter changes by transforming the complex resource cessation planning problem into a set of learnable parameters through machine learning. Instead of implementing complex computational algorithms during client interactions, the system learns optimal parameter relationships during training (e.g., relationships between client age, risk tolerance, and resource allocation). During deployment, only these learned parameters are applied, significantly reducing runtime complexity while maintaining high reliability.
3Ease of operation
If customized product offerings with interactive charts are provided, then the user experience and client satisfaction improves, but the time and computational resources required worsen
Solution Approach 1:
The patent replaces manual product offering generation and chart creation with automated machine learning systems. The ML models automatically generate customized product recommendations and generate interactive visualizations based on client inputs and model predictions. This substitution eliminates time-consuming manual processes while providing comprehensive, personalized product offerings with interactive charts that enhance user experience.
Solution Approach 2:
The patent implements self-service by enabling the system to automatically generate customized product offerings and interactive visualizations without requiring manual intervention. The machine learning models autonomously process client data, generate personalized recommendations, and create interactive charts that clients can explore. This self-service capability reduces the time and computational resources needed while maintaining high ease of operation and client satisfaction.
Data Source
AI summary
One embodiment of a computer-implemented method may include receiving one or more target parameters associated with a client, where the one or more target parameters may be based on one or more factors. The method may further include determining a risk tolerance measurement for the client associated with achieving the target parameter(s). The method may further include determining an optimal source quantity associated with achieving the target parameter(s) based on the risk tolerance measurement. The method may further include determining optimal allocation parameters for allocating the optimal source quantity. The method may further include generating a user interface including one or more duration-dependent hybrids, where the one or more duration-dependent hybrids may include one or more product offerings based at least in part on the optimal source quantity and the optimal allocation parameters.