Digital Data Processing System for Interaction Tracking
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Solution Overview
Problem
Current systems face challenges in accurately tracking, analyzing, and reporting interactions between individuals and entities, such as retailers and their customers, due to errors in recorded attributes like email addresses and phone numbers, and difficulties in distinguishing buying groups across various payment methods and touchpoints.
Innovation Solution
A digital data processing system that analyzes interactions by grouping data into 'data blobs' based on predesignated attributes like credit card numbers and email addresses, and newly designates attributes such as phone numbers that reliably demarcate individuals and their affiliated groups, using a combination of predesignated and newly identified attributes for refined grouping and unique identifier assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional methods using registration logs and point-of-sale attributes are used, then data collection is simplified, but measurement precision deteriorates due to errors in recorded attributes like email addresses and phone numbers
Solution Approach 1:
The system uses feedback mechanisms to validate and correct recorded attributes. By analyzing patterns across multiple data points and comparing against expected formats and ranges, the system automatically detects and corrects errors in email addresses, phone numbers, and other attributes, thereby improving measurement precision while maintaining ease of data collection
Solution Approach 2:
The patent replaces manual verification processes with automated computational methods. Instead of relying on manual checking of registration logs and point-of-sale records, the system uses algorithmic validation, pattern recognition, and cross-referencing across multiple data sources to automatically identify and correct attribute errors
2Measurement precision
If multiple attributes are collected to improve identification accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system segments the data processing task into distinct modules: data collection, validation, attribute weighting, and identification. By dividing the complex process into manageable segments, each handled by specialized algorithms, the system achieves high measurement precision while keeping the overall device complexity manageable through modular architecture
Solution Approach 2:
The patent dynamically adjusts the weight and significance of different attributes based on their reliability and relevance in specific contexts. By changing parameter weights rather than simply adding more attributes, the system improves identification accuracy while avoiding the complexity increase that would result from collecting and processing additional data types
3Measurement precision
If manual verification of attributes is performed, then measurement precision improves, but loss of time increases
Solution Approach 1:
The system performs self-service verification by automatically validating attributes against each other and against expected formats without human intervention. The automated system cross-checks email addresses, phone numbers, and other attributes, correcting errors and confirming accuracy independently, thereby eliminating the time loss associated with manual verification while maintaining high measurement precision
Data Source
AI summary
The invention provides in some aspects methods of digital data processor-based analysis of digital data that represent interactions to identify distinct individuals and/or the entities with which they are affiliated (e.g., households, businesses, social or other groups) involved in those interactions. The methods can be employed, for example, to analyze digital data representing retail purchase, marketing and visitor interactions for tracking and/or reporting purposes.