Concurrent Gradient Descent for Financial Plan Optimization

The patent segments the computation process into two distinct phases: (1) a brute force phase that comprehensively evaluates all possible plans to identify candidate plans with high success probabilities, and (2) a concurrent gradient descent phase that efficiently optimizes among these candidates to determine the optimal plan. This segmentation allows the system to maintain comprehensive coverage while reducing overall computing time and resources by applying different computational strategies to different stages of the problem.

The patent performs preliminary action by first using brute force computing to identify and filter candidate plans with the highest probabilities of success before applying the more efficient concurrent gradient descent optimization. This preliminary filtering step reduces the search space for subsequent optimization, allowing the system to achieve comprehensive coverage of possibilities while minimizing the computational resources required for the final optimization phase.

The patent implements dynamics by transitioning from static sequential optimization to dynamic concurrent optimization. The concurrent gradient descent algorithm performs multiple optimization steps in parallel, allowing the system to explore multiple solution paths simultaneously and converge to the optimal plan more quickly. This dynamic approach maintains optimization accuracy while dramatically reducing processing time compared to conventional sequential methods.

The patent changes the computational parameter from sequential processing to concurrent parallel processing. By executing multiple gradient descent steps concurrently rather than sequentially, the system achieves the same optimization accuracy with significantly reduced processing time. This parameter change transforms the computational approach while maintaining the mathematical rigor needed for accurate optimization.

The patent segments the complex multi-variable optimization problem into manageable phases: first using brute force to evaluate all combinations of personal variables (lifestyle, age, income, retirement date, lifespan) to identify candidate plans, then using concurrent gradient descent to optimize among these candidates. This segmentation handles computational complexity by applying different algorithms to different aspects of the problem while maintaining personalized accuracy through comprehensive evaluation of all variable combinations.

The patent performs preliminary action by first comprehensively evaluating all combinations of personal variables to identify candidate plans with high success probabilities before applying optimization. This preliminary phase handles the computational complexity of multiple personal variables by systematically evaluating them all, then reduces the complexity for the final optimization step by working only with the identified candidate plans rather than all possible combinations.