Location Data Aggregation for User Tracking
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Solution Overview
Problem
Entities face challenges in tracking user locations efficiently and accurately, leading to redundant tracking, resource wastage, and privacy infringement, with existing methods providing unreliable location information.
Innovation Solution
A location platform aggregates data from transaction devices, mobile user devices, and stationary user devices using weights and timestamps, processed with a machine learning model to predict user locations, providing general location information to third parties without revealing precise locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entities track user locations using traditional methods, then location information can be obtained, but computing resources are wasted and privacy is infringed due to redundant tracking
Solution Approach 1:
The patent combines location data from multiple sources (transaction devices, mobile user devices, and stationary user devices) into a unified location determination system. By merging these data sources and applying aggregation algorithms with weights and timestamps, the system achieves accurate location tracking while eliminating redundant tracking efforts across different entities, thus conserving computing resources.
2Measurement precision
If precise user location data is collected from multiple sources, then location accuracy improves, but user privacy is compromised
Solution Approach 1:
The patent extracts only the necessary location information from multiple data sources without collecting or storing unnecessary personal data. The system processes location data from transaction devices and user devices to determine user location, then provides only general location information to third parties, extracting and providing only what is essential while leaving out detailed personal location history and sensitive information.
Solution Approach 2:
The patent transforms precise location data into general location information by changing the granularity parameter. Instead of providing exact coordinates or specific addresses to third parties, the system aggregates location data and provides generalized location ranges or areas, thus maintaining measurement precision for internal purposes while reducing privacy infringement in external disclosures.
3Reliability
If multiple data sources are aggregated for location determination, then location reliability improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary location platform that mediates between multiple data sources (transaction devices, mobile user devices, stationary user devices) and third parties. This intermediary system consolidates the complexity of data aggregation, weight assignment, timestamp processing, and location determination in one central location, simplifying the overall system architecture while improving location reliability through multi-source data integration.
Data Source
AI summary
A device receives, from a transaction device, transaction data associated with a transaction performed by a user, and receives first location data indicating a location of a mobile user device. The device receives, from a stationary user device, browser data associated with online activity of the user, and determines, based on the browser data, second location data indicating a location of the stationary user device. The device determines, based on the transaction data, third location data indicating a location of the transaction device, and assigns weights and time stamps to the first, second, and third location data. The device aggregates the first, second, and third location data, based on the weights and the time stamps, to generate aggregated location data. The device processes the aggregated location data, with a model, to predict a particular location of the user, and performs actions based on the particular location.