Pricing Information System Using Transaction Data
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Solution Overview
Problem
Users face challenges in accessing accurate pricing information from merchants, as this information is often not tracked or shared due to competitive concerns.
Innovation Solution
A system and method that collects, categorizes, and generates pricing information from credit card transactions, providing average transaction prices and graphical data for merchants or potential purchasers, allowing users to analyze and compare prices across multiple merchants or locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If merchants do not share pricing information, then competitive advantage is maintained, but price transparency is reduced
Solution Approach 1:
The patent introduces a third-party intermediary system that collects pricing data from multiple merchants anonymously and presents aggregated price information to consumers. This intermediary acts as a mediator that enables price transparency without directly exposing individual merchants' pricing strategies to competitors, thus resolving the contradiction between maintaining competitive advantage and providing price transparency.
2Measurement precision
If merchants track detailed transaction data, then pricing accuracy is improved, but data collection complexity increases
Solution Approach 1:
The system utilizes multi-functional credit card transaction data that already contains pricing, timing, and merchant information. By leveraging this existing universal data source that serves multiple purposes (billing, analytics, marketing), the system achieves accurate pricing information without requiring separate complex data collection infrastructure, thus resolving the contradiction between pricing accuracy and data collection complexity.
3Productivity
If pricing information is aggregated from multiple sources, then market competitiveness is improved, but information processing complexity increases
Solution Approach 1:
The patent segments the aggregation process into distinct functional modules: data collection from multiple merchants, anonymous identification of merchants, categorization of pricing data, and presentation to consumers. This segmentation allows each module to handle specific tasks independently, improving market competitiveness through comprehensive data aggregation while managing processing complexity through modular architecture.
4Adaptability or versatility
If merchants remain anonymous in pricing data, then competitive protection is improved, but data utility is reduced
Solution Approach 1:
The system applies local quality by providing different levels of information identification to different users. Individual merchants remain anonymously identified in the aggregated data to protect their competitive position, while the system simultaneously maintains the ability to identify and analyze specific merchant performance for internal business decisions. This differentiated identification approach preserves both competitive protection and data utility.
Data Source
AI summary
A system and method uses categorized transaction data to respond to requests for pricing information about one or more merchants corresponding to a subset of the transaction data.


