Retail Interaction Platform Anonymized Data Integration
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Solution Overview
Problem
Current data analytic platforms struggle to effectively leverage anonymized data from Internet of Things (IoT) devices and sensors in retail environments, limiting their ability to provide comprehensive customer interaction analytics without being intrusive or requiring user identifiable information.
Innovation Solution
The Retail Interaction Platform (RIP) integrates data from multiple IoT devices and sensors, using technologies like MAC address sniffers, beacons, and RFID readers to collect and analyze customer interactions in a non-intrusive manner, storing data on a network-based platform for real-time analytics and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If user-identifiable data is collected through Wi-Fi login processes, then customer information quality is improved, but customer privacy and intrusiveness worsen
Solution Approach 1:
The patent extracts and removes personally identifiable information (PII) from collected data, keeping only anonymized identifiers like device MAC addresses and behavioral patterns. This allows the system to maintain useful customer interaction data while eliminating privacy-intrusive elements, resolving the contradiction between information quality and privacy protection
Solution Approach 2:
The patent introduces anonymized device identifiers as an intermediary between the customer and the retailer's data system. These identifiers allow tracking and analysis of customer behavior without directly exposing personal information, serving as a mediator that preserves analytical value while protecting privacy
2Loss of information
If anonymized data from IoT devices is processed, then data coverage and analytics capability are improved, but data format standardization and integration difficulty worsen
Solution Approach 1:
The patent implements a universal data processing framework that handles multiple IoT device types and communication protocols through a single standardized interface. The system processes diverse data sources (Wi-Fi devices, Bluetooth beacons, RFID readers, sensors) using common anonymization and aggregation techniques, eliminating the need for device-specific processing pipelines and reducing integration complexity
Solution Approach 2:
The patent transforms diverse IoT device data into a standardized parameter set focusing on anonymized identifiers, temporal patterns, and behavioral metrics. By converting various data formats into consistent parameter structures, the system achieves broad data coverage while simplifying integration across different device types
3Object-affected harmful factors
If passive data collection methods are used, then customer intrusiveness is reduced, but data accuracy and identifiability worsen
Solution Approach 1:
The patent combines multiple passive data collection streams (Wi-Fi MAC addresses, Bluetooth beacon interactions, RFID readings, sensor data) to compensate for the limitations of individual passive methods. By merging these streams and analyzing patterns across them, the system maintains low intrusiveness while achieving accurate customer behavior identification through aggregated evidence
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that relate to obtaining data that indicates a time that a MAC address was detected at a temporary site, obtaining data that indicates a time that a MAC address was detected at a non-temporary site, obtaining data that indicates that a first interaction with a first item occurred at the temporary site, obtaining data that indicates that a second interaction with a second item occurred at the non-temporary site, determining, that the MAC addresses match, determining that the time that the MAC address was detected at the temporary site occurred before the time that a MAC address was detected at the non-temporary site, determining that the first item and the second item match, and classifying the detection of the MAC address at the non-temporary address as being caused by the temporary site.