Savings Route Calculation Using Graph Search and Circle of Influence
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Solution Overview
Problem
Current portable computing devices lack functionality to efficiently plan trips that incorporate both navigational needs and promotional offers from merchants, failing to optimize routes for savings and consumer benefits.
Innovation Solution
A method and system that uses a graph search algorithm to calculate and display 'savings routes' on portable computing devices, incorporating circle of influence data from merchant offers, allowing users to select routes based on distance, number of routes, and minimum savings value, thereby enhancing trip efficiency and consumer benefits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional navigation software is used, then basic directional guidance is provided, but it fails to optimize routes for savings and consumer benefits
Solution Approach 1:
The navigation system is enhanced to perform multiple functions: traditional directional guidance plus savings route optimization. The system integrates merchant offer data, consumer preference profiles, and dynamic route calculation to provide comprehensive trip planning that simultaneously considers navigation, savings, and consumer benefits.
Solution Approach 2:
The route calculation algorithm dynamically adjusts routing parameters based on multiple factors including distance, time, consumer preferences, and available merchant offers. The system transforms static navigation into dynamic optimization by continuously evaluating different route parameters to maximize savings while meeting consumer needs.
2Adaptability or versatility
If merchants provide promotional offers, then consumer attraction increases, but route planning complexity increases
Solution Approach 1:
Consumer preference profiles and merchant offer data are pre-configured and stored before trip planning. The system performs preliminary data preparation by organizing merchant information, consumer preferences, and offer details into structured formats that can be quickly processed during route calculation, reducing real-time computational complexity.
Solution Approach 2:
The system uses simplified data models and representations of complex merchant offer structures. By creating abstracted versions of promotional data that capture essential information without full complexity, the system enables efficient route optimization while maintaining accurate representation of merchant offers.
3Adaptability or versatility
If multiple route options are calculated, then user choice increases, but calculation time increases
Solution Approach 1:
The system calculates a limited number of top-ranked route options rather than all possible routes. By focusing computational resources on generating the most promising routes based on consumer preferences and savings potential, the system provides sufficient user choice while maintaining fast calculation times.
Solution Approach 2:
Different portions of the route calculation process use different levels of detail and computational intensity. The system applies refined optimization algorithms only to critical route segments where savings opportunities exist, while using simpler methods for straightforward navigation segments, thereby reducing overall calculation time while maintaining route option quality.
Data Source
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AI summary
A method and system for calculating savings routes for display on a portable computing device (PCD) are described. The method includes receiving at least one of a product category and a service category from an operator of a PCD. The PCD may also receive a destination address. With this information, circle of influence data based on an offer for at least one product or service corresponding to the product category or service category may be generated and provided to the PCD. The circle of influence data may impact edge weights of a graph search algorithm. The graph search algorithm solves a single-source shortest path problem for a graph with non-negative edge path costs. The circles of influence in combination with the graph search algorithm allow a PCD to calculate one or more savings routes based on a start point and the desired destination address provided by the operator of the PCD.