Structure Learning Parser for Purchase Data Extraction
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Solution Overview
Problem
The diversity of payment confirmation methods and merchants makes it difficult for individuals to track their purchases and for merchants to obtain comprehensive customer purchase history data, limiting targeted marketing efforts.
Innovation Solution
A system and method for extracting product purchase information from electronic messages using a structure learning parser that automatically learns message formats, allowing for precise extraction and aggregation of purchase data across various formats and languages, enabling enhanced purchase history visualization and targeted marketing strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If purchase confirmations are sent in diverse formats by different merchants, then merchants can use their preferred communication channels, but it becomes difficult and expensive to extract product purchase information from these confirmations
Solution Approach 1:
The patent applies universality by creating a unified data extraction system that handles multiple confirmation formats (email, SMS, mobile app notifications) through a single platform. The system uses standardized data fields and a common processing architecture to extract purchase information from diverse merchant confirmation types, eliminating the need for separate extraction mechanisms for each format while maintaining the ability to accommodate future formats through configurable templates.
2Adaptability or versatility
If purchase confirmations are sent in diverse formats by different merchants, then merchants can operate independently, but it becomes expensive to extract product purchase information from purchase confirmations
Solution Approach 1:
The patent applies copying by creating standardized data templates and extraction patterns that can be replicated across different merchant confirmations. Instead of developing custom extraction logic for each merchant or format, the system uses template-based copying where a single extraction rule set can process multiple confirmation types. This reduces development and maintenance costs while preserving merchant independence in their confirmation delivery methods.
3Reliability
If purchase information is tracked only by the issuing merchant, then the merchant maintains control of its data, but insufficient purchase history data is obtained for developing accurate customer profiles
Solution Approach 1:
The patent applies merging by consolidating purchase information from multiple merchants into a unified customer profile through a centralized data aggregation system. The system combines purchase histories across different merchants while maintaining data security and customer privacy, enabling comprehensive customer profiles that reflect cross-merchant purchasing behavior. This allows merchants to benefit from aggregated insights without compromising their individual data control or security.
Data Source
AI summary
Improved systems and methods for extracting product purchase information from electronic messages transmitted between physical network nodes to convey product purchase information to designated recipients. These examples provide a product purchase information extraction service that is able to extract product purchase information from electronic messages with high precision across a wide variety of electronic message formats and thereby solve the practical problems that have arisen as a result of the proliferation of different electronic message formats used by individual merchants and across different merchants and different languages. In this regard, these examples are able to automatically learn the structures and semantics of different message formats, which accelerates the ability to support new message sources, new markets, and different languages.