Sensor Network Tracking Without Opt-In Consent
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Solution Overview
Problem
Current methods for customer tracking in retail environments are limited in their ability to gather information on customers who have not opted in, as they require explicit consent and cannot associate identity information with mobile devices without user awareness or participation.
Innovation Solution
A system and method that utilizes a distributed network of sensors and mobile device applications to track user device information, even without user consent, by detecting proximity and associating identity through transaction data or incentives, and stores this information for later analysis to infer customer interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If explicit opt-in consent is required for tracking, then customer privacy is protected, but tracking coverage and data collection completeness deteriorate
Solution Approach 1:
The system performs preliminary tracking of user devices before identity association is established. Sensors continuously collect device location data, and when a user later provides consent or identity information becomes available, the pre-collected data is automatically associated with that identity, eliminating the need to miss any tracking opportunities during the opt-in process.
Solution Approach 2:
The system uses mobile device identifiers as an intermediary between anonymous sensor data and user identity. The tracking system first captures device-level data without requiring user consent, then later associates this data with user identities when consent is provided or identity information becomes available through other channels, thereby maintaining both coverage and privacy compliance.
2Object-affected harmful factors
If user consent is required before tracking, then user privacy rights are respected, but the ability to analyze behavior patterns of all customers deteriorates
Solution Approach 1:
The system collects and stores anonymous device-level behavioral data in advance, maintaining detailed movement patterns, dwell times, and location information. When users later provide consent or identity information, this pre-collected behavioral data is associated with their identities, ensuring complete behavioral analysis capability without requiring upfront consent.
Solution Approach 2:
The system employs device identifiers as intermediaries to bridge anonymous tracking data and user identities. This allows the system to maintain complete behavioral data collection while respecting privacy rights, as the association between behavior and identity only occurs when appropriate consent or identification is obtained.
3Ease of operation
If tracking only occurs after opt-in, then user awareness and consent are ensured, but tracking continuity and movement pattern accuracy deteriorate
Solution Approach 1:
The system establishes continuous tracking from the moment a user enters the facility, capturing complete movement trajectories, stop durations, and path patterns. This preliminary tracking continues uninterrupted, and when consent is provided, the complete movement data is associated with the user, providing accurate movement pattern analysis without gaps that would occur if tracking only began after opt-in.
Data Source
AI summary
An embodiment of a system comprises one or more processors; and one or more memories adapted to store machine-readable instructions which when executed by the processor(s) cause the system to: receive tracked user device information from a distributed network of sensors configured for tracking user device information associated with one or more user devices of corresponding users in a proximity of the distributed network of sensors, wherein the user device information is tracked even when the corresponding users have not opted in the one or more user devices to be tracked by the distributed network of sensors; store the tracked user device information in a tracking database, wherein the tracked user device information is stored even for corresponding users that have not been identified and is for later use when such corresponding users are identified; and analyze the tracked user device information to infer interests of the corresponding users.