Compressed Filter System for Real-Time Customer Eligibility
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Solution Overview
Problem
Existing systems face challenges in providing real-time campaign eligibility checks for customers, as brute force lookups require extensive computing resources, making it inefficient for real-time results, especially when offering promotions on web pages.
Innovation Solution
Implementing a compressed filter system, such as Bloom filters, to efficiently determine customer eligibility by hashing customer lists, reducing data size by 60-70% and enabling real-time lookups with lower hardware and operational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If brute force lookup is performed against a database containing all campaign users, then real-time campaign eligibility results can be provided, but extensive computing resources are required
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing hash values of campaign user lists in a compressed filter data structure before real-time lookups. This allows the system to perform fast eligibility checks without repeatedly processing the entire user database during runtime, thus achieving real-time performance with reduced computing resources.
Solution Approach 2:
The patent creates a compressed filter as a simplified copy of the full campaign user database. Instead of querying the complete database during real-time operations, the system uses this compressed representation that contains essential eligibility information in a more efficient format, reducing the computational burden while maintaining lookup functionality.
2Quantity of substance
If a compressed filter system is implemented to reduce data size, then hardware and operational costs are reduced, but the system complexity increases due to hashing and filter construction
Solution Approach 1:
The patent applies parameter changes by transforming the campaign user data into a different representation format using hash functions. The compressed filter stores hashed values rather than raw user records, fundamentally changing the data parameters to achieve compression. This transformation reduces data size while the hashing mechanism provides a systematic way to manage the complexity of the compression process.
Data Source
AI summary
Embodiments of methods and systems for dynamic messaging are disclosed. A lookup request may be received for a customer. A plurality of eligible campaigns may be determined for the customer. An eligible campaign of the plurality of eligible campaigns may include an available offer for the customer. The available offer may be represented by a compressed filter. The customer may match the compressed filter of the available offer of the plurality of eligible campaigns. The available offer of a campaign may be selected from the plurality of eligible campaigns based on metadata of the plurality of eligible campaigns and campaign history data of the customer. A message associated with the selected offer may be provided. The message may be capable of being presented to the customer.