Heuristic Coupon Combination Algorithm for Retail Checkout
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Solution Overview
Problem
Current solutions for optimizing coupon combinations during transactions are computationally expensive and impractical, leading to inaccurate results due to the factorial complexity of evaluating all possible combinations, especially in retail environments with multiple items.
Innovation Solution
A processor and memory-efficient heuristic algorithm that sorts items into zones based on coupon conditions, iteratively groups items, and uses recursive processes to identify optimal combinations, reducing the need to explore every possible combination by focusing on the most promising ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all possible coupon combinations are evaluated to find the optimal mix, then the accuracy of the solution is improved, but the processing time becomes unacceptable for retail environments
Solution Approach 1:
The patent segments the problem by dividing items into different zones (e.g., high-value items, low-value items, items with specific categories) and applies different coupon strategies to each zone. This segmentation allows the system to evaluate fewer combinations while still finding near-optimal solutions, resolving the contradiction between accuracy and processing time.
Solution Approach 2:
The patent applies partial action by evaluating only the most promising coupon combinations rather than all possible combinations. It uses heuristics to identify and evaluate only the top-k combinations, achieving sufficient accuracy without the computational expense of exhaustive search.
2Adaptability or versatility
If the number of items in a transaction increases, then the complexity of finding the best coupon mix increases factorially, but cashiers cannot preplan optimal combinations
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing coupon eligibility information, item categories, and discount rules in a database before transactions occur. During checkout, the system quickly retrieves and applies pre-computed information rather than performing complex calculations in real-time, enabling scalability to transactions with any number of items.
Solution Approach 2:
The patent changes parameters by transforming the factorial complexity problem into a polynomial-time solution through parameter optimization. It uses constraints on combination size, item categories, and coupon types to reduce the search space from factorial to manageable polynomial complexity, allowing the system to adapt to transactions of any size without exponential resource requirements.
3Measurement precision
If traditional software-based solutions are implemented to solve optimal coupon combinations, then the computational accuracy is improved, but the processing speed becomes too slow for retail checkouts
Solution Approach 1:
The patent substitutes the traditional mechanical approach of exhaustive enumeration with a heuristic-based algorithmic system. It replaces the brute-force mechanical search with intelligent pruning rules, zone-based grouping, and priority queues that achieve near-optimal results at polynomial time complexity, simultaneously improving both accuracy and processing speed.
Solution Approach 2:
The patent introduces dynamics by making the evaluation process adaptive and iterative. It dynamically adjusts which combinations to evaluate based on running totals, zone boundaries, and eligibility constraints, allowing the system to converge on optimal solutions quickly without fixed predetermined evaluation sequences, thereby improving both accuracy and speed.
Data Source
AI summary
A set of values and a condition are provided. The values are sorted and placed in zones based on the condition and first rules. The values are grouped into combinations within select ones of the zones based on second rules while other zones are excluded. Sizes of the combinations are modified and the values are regrouped into new combinations and re-evaluated in view of the second rules. A final combination of the values is identified using third rules. The final combination representing an optimal grouping of the values based on the condition.