Predictive Bounding for Low-Latency Discount Transactions
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Solution Overview
Problem
Existing computing systems are inadequate for distributed applications where data acquisition exceeds the latency budget, leading to delayed or impaired responses and out-of-tolerance process results in combinatorial optimization tasks, such as discount transactions, due to high-latency interfaces and varied discount requirements.
Innovation Solution
A computer-implemented method dynamically optimizes discount transactions by determining associations between discounts and items, breaking transactions into constituent parts, and adjusting discounts based on unmet criteria to provide the lowest aggregate price, using a predictive bounding approach to manage latency and optimize user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is acquired through high-latency interfaces to perform combinatorial optimization, then optimization accuracy is improved, but response time exceeds the latency budget
Solution Approach 1:
The system performs preliminary actions by pre-fetching and caching data that is likely to be needed for future optimization requests. Data is acquired and stored in advance through high-latency interfaces before it is actually needed, so that when a request arrives, the data is already available in fast memory, avoiding the latency penalty at request time.
Solution Approach 2:
The optimization process is segmented into multiple stages: data acquisition, data processing, and optimization computation. By dividing the workflow and overlapping these stages (pipelining), the system can initiate data acquisition early while preparing computation resources, reducing the critical path delay and improving overall response time without sacrificing optimization accuracy.
2Reliability
If combinatorial optimization is performed with complete data, then optimization quality is improved, but processing complexity increases
Solution Approach 1:
The system applies partial action by performing optimization on a subset of most relevant features or data points rather than processing all available data. This selective approach maintains optimization quality for critical parameters while reducing overall processing complexity and computational resources required.
Solution Approach 2:
Data preprocessing and feature selection are performed in advance to transform raw data into optimized formats and identify only the most relevant features for the specific optimization task. This preliminary preparation reduces the complexity of the main optimization algorithm while preserving the quality of results by ensuring only pertinent data is processed.
3Measurement precision
If multiple discounts from different merchants are combined, then price optimization is improved, but transaction complexity increases
Solution Approach 1:
The system merges multiple discount opportunities from different merchants into a single unified discount transaction. By consolidating what would otherwise require multiple separate transactions and interactions with different merchants, the system achieves the same price optimization effect while simplifying the user experience to a single transaction flow.
Solution Approach 2:
The system acts as an intermediary that automatically negotiates and coordinates between multiple merchants and the user. It handles the complexity of combining discounts, verifying eligibility, and coordinating transactions in the background, while presenting a simplified interface to the user that appears as a single straightforward transaction.
Data Source
AI summary
Disclosed are techniques for achieving high-volumes of low-latency price plus discount transactions that are dynamically created through transaction optimization. Aspects may include combining and optimizing different types of discounts into a single discount and dynamically controlling the single discount through a single user-facing transaction. A computer model may determine one or more transactions and sets of discounts applicable to those transactions upon which the user-facing transaction and single discount are based. Associations between discounts and items may be determined for at least some different types of discounts to apply a set of discounts including at least two different discounts of different discount types to a desired transaction for one or more items. Aspects may include determining a discount transaction comprising a plurality of constituent transactions that form a single user-facing transaction.