Predicate Logic Tree for Fast Retail Offer Eligibility
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Solution Overview
Problem
Simple rules-based systems for determining offer eligibility are limited in processing power and cannot effectively apply complex logic rules, particularly at the point-of-sale, leading to inefficiencies in determining consumer eligibility for retailer rewards programs.
Innovation Solution
A predicate logic tree system that associates consumer records directly with child rules, allowing for the evaluation of relevant consumer data to determine eligibility, incorporating sorting and variable resolution to apply complex retailer reward program offerings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If consumer records are associated with parent rules of the logic tree, then the system structure is simpler, but processing speed is inhibited
Solution Approach 1:
The patent segments the logic tree into parent rules and child rules, and segments consumer records into current purchase records and historical purchase records. By associating different types of records with different rule levels, the system achieves both structural simplicity and fast processing. Current purchase records are associated with child rules for immediate evaluation, while historical records are associated with parent rules for contextual evaluation, resolving the contradiction between structure and speed.
2Ease of manufacture
If simple rules-based systems are used, then the system is easier to implement, but it cannot apply complex logic rules
Solution Approach 1:
The patent implements a nested logic tree structure where parent rules contain child rules. This nesting allows the system to handle complex logic by organizing rules hierarchically - simple child rules handle immediate purchase conditions while parent rules manage historical context and complex eligibility criteria. The nested structure enables complex logic application while maintaining ease of implementation through systematic organization.
Solution Approach 2:
The system performs preliminary actions by pre-processing consumer records and pre-evaluating simple child rules before applying complex parent rules. Historical purchase records are analyzed in advance to determine eligibility for complex offers, while current purchase records are evaluated immediately. This preliminary action separates simple from complex processing, maintaining implementation ease while enabling complex logic application.
3Loss of time
If the system performs entire analysis at the point-of-sale, then response time is minimized, but processing power is insufficient for complex rules
Solution Approach 1:
The patent segments the analysis into two phases: offline pre-processing of historical purchase records and online evaluation of current purchase records. The pre-processing phase, which requires significant processing power, is performed beforehand to determine eligibility for complex offers. The online phase at point-of-sale only needs to evaluate simple child rules against current purchases, minimizing response time while the accumulated processing power from pre-processing enables complex rule application.
Data Source
AI summary
Example computer executable instructions for determining a consumer's offer eligibility using a predicate logic tree against sets of input data are provided. In particular, the disclosed examples recite a rewards engine capable of determining a consumer's offer eligibility using a predicate logic tree against sets of input data. The rewards engine applies rules that are relevant to a particular deal offered by a retail establishment to the consumer's records to determine the consumer's offer eligibility. In some examples, the rewards engine sorts or partially sorts some or all of the consumer's records to determine the consumer's offer eligibility.