Contextual Coupon Engine Using Association Rules
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Solution Overview
Problem
Existing personal assistant technologies fail to provide users with relevant coupons at the right time and location, based on their specific interests and contexts, leading to inefficiencies in coupon delivery and user engagement.
Innovation Solution
A system that utilizes association rules generated by analyzing user actions and machine learning algorithms to determine when a user is likely interested in a coupon, presenting it when their context matches the rule, through a personal assistant, web browser, or other applications on a user's device, allowing vendors to add coupons and manage their distribution effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If coupons are distributed broadly to maximize vendor reach, then vendor reach is improved, but coupon relevance and user engagement deteriorate
Solution Approach 1:
The patent segments the broad user base into distinct clusters based on behavioral patterns, demographics, and contextual data. Each segment receives customized coupon sets tailored to their specific interests and shopping habits, thereby maintaining high relevance while expanding overall vendor reach through multi-segment distribution.
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns and contextual information before coupon distribution. Association rules are pre-generated based on historical data, enabling the system to predict user interest and deliver relevant coupons in advance of shopping decisions, thus maintaining relevance even as distribution scale increases.
2Loss of information
If association rules are generated by analyzing all user actions and contextual data, then coupon relevance is improved, but system complexity and computational requirements worsen
Solution Approach 1:
The patent extracts only the most salient behavioral patterns and contextual features from extensive user data using association rule mining. Rather than processing all raw data, the system identifies and extracts key predictive patterns that drive coupon relevance, significantly reducing computational complexity while maintaining high relevance accuracy.
Solution Approach 2:
The system transforms complex multi-dimensional user behavior data into simplified association rules with defined confidence thresholds and support metrics. By changing the parameter representation from raw behavioral data to structured rule-based predictions, the system reduces computational complexity while preserving the essential information needed for relevant coupon delivery.
3Measurement precision
If coupons are presented only when context perfectly matches association rules, then coupon precision is improved, but coupon delivery frequency and user engagement worsen
Solution Approach 1:
The patent implements partial matching logic where coupons are delivered when association rules meet minimum confidence thresholds rather than requiring perfect context matches. This partial action approach delivers coupons in situations of high but not absolute certainty, increasing delivery frequency and engagement while maintaining acceptable precision through threshold-based filtering.
Data Source
AI summary
Aspects of the technology described herein provide a more efficient user interface by providing coupons that are tailored to a specific user's interests. The coupons may be provided by a personal assistant or some other application running on a user's computing device. A goal of the technology described herein is to provide relevant coupons when the user can and actually wants to use them. The coupons are designed for goods or services the user intends to purchase.