Location Trigger Prediction Using Transaction Data Streams
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Solution Overview
Problem
Current systems face challenges in initiating location-triggered communication with users due to privacy concerns related to mobile device location information, leading to limitations in employing location-based communication strategies.
Innovation Solution
The system predicts a location trigger based on mobile device location-independent user location information from data streams, such as transaction data, using a prediction model to determine when a user is likely to be available at a destination location, allowing for communication initiation without relying on device location information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If mobile device location information is used to determine user location, then location-triggered communication can be initiated, but privacy-related concerns prevent users from sharing such information
Solution Approach 1:
The patent introduces third-party location data sources (e.g., point of interest data, social media check-ins, transaction records) as intermediaries to infer user location without requiring direct access to mobile device GPS data. These intermediaries enable location-triggered communication while respecting user privacy by avoiding direct collection of sensitive device location information.
Solution Approach 2:
The patent creates indirect copies of location information from alternative data sources rather than using direct device location data. By copying location context from point of interest databases, social media activity records, or transaction logs, the system can infer user presence and initiate appropriate communications without accessing or storing sensitive mobile device location information.
2Productivity
If device location information is collected for communication purposes, then communication efficiency can be improved, but user privacy is compromised
Solution Approach 1:
The patent extracts location-related insights from non-sensitive data sources such as point of interest databases, social media check-in records, and transaction logs. By taking out location context from these alternative sources rather than from device location APIs, the system maintains communication efficiency while avoiding the collection and processing of sensitive user privacy information.
Solution Approach 2:
The patent changes the data parameters used for location determination from direct device GPS coordinates to indirect inference parameters such as point of interest proximity, social media activity patterns, and transaction temporal-spatial data. This parameter transformation enables communication efficiency through location-aware timing while preserving user privacy by working with less sensitive data types.
3Measurement precision
If location data from multiple sources is processed, then prediction accuracy of user availability can be improved, but system complexity increases
Solution Approach 1:
The patent segments the location data processing into distinct modules: point of interest data processing, social media data processing, and transaction data processing. Each module handles specific data types independently, improving prediction accuracy through multi-source analysis while managing system complexity through modular architecture that allows selective activation of data sources based on availability and relevance.
Data Source
AI summary
In certain embodiments, initiation of a location-triggered communication with a user may be facilitated. Communication may be initiated with a user (e.g., on a mobile device) based on an occurrence of a location trigger corresponding to a destination location at which the communication is to be initiated. The occurrence of the location trigger indicates the user is likely on the way to the destination location. The occurrence may be predicted based on mobile device location-independent user location information obtained from a data stream of a real-time application. For example, an event location and an event time of an event associated with the user may be extracted from the data stream and used to predict the occurrence.