GPS Offer Relevance via Local Quality Filtering
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Solution Overview
Problem
Conventional personalized recommendations based on GPS data are often irrelevant due to distance from the individual's location, as they do not account for real-time and historical purchasing behavior effectively.
Innovation Solution
A central system uses GPS data from a mobile device to predict likely purchases by an individual, generating offers from nearby merchants based on transaction history and location, and sends these offers in real-time to the mobile device for display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If personalized recommendations are generated based on conventional methods, then recommendations can be provided to individuals, but the recommendations are not relevant because they are from merchants located distant from the individual
Solution Approach 1:
The system applies local quality by filtering merchant offers based on geographic proximity to the individual's current location. The central system receives real-time GPS data, determines the individual's location, and selectively generates recommendations only from merchants within a threshold distance, ensuring local relevance while maintaining personalized recommendation capabilities
Solution Approach 2:
The system performs preliminary action by pre-processing GPS data through cleansing operations (removing duplicates, outliers, and invalid points) and pre-calculating merchant locations and categories before generating recommendations. This preparation ensures that when recommendations are needed, the system can quickly filter and select relevant local offers without computational delays
2Productivity
If GPS data is collected and processed in real-time, then relevant offers can be generated, but the system complexity increases
Solution Approach 1:
The system segments the complex task of real-time recommendation generation into distinct modular components: GPS data collection module, data cleansing module (with sub-functions for removing duplicates, outliers, and invalid points), location determination module, merchant filtering module, and offer generation module. Each module handles a specific aspect of the process, reducing overall system complexity while enabling real-time operation
Solution Approach 2:
The central system acts as an intermediary between the mobile device (which collects GPS data) and the merchants (which provide offers). It mediates the complex processing by receiving GPS data, cleansing and validating it, determining location, filtering merchant offers based on proximity and relevance, and delivering personalized recommendations, thereby simplifying the interaction between mobile devices and merchants
3Measurement precision
If GPS data cleansing is performed to remove variability, then data quality improves, but processing time increases
Solution Approach 1:
The system applies partial action by selectively cleansing GPS data based on identified quality issues. Rather than processing all data points equally, it targets specific problems: removing duplicate points, filtering outliers beyond threshold values, and eliminating invalid GPS points. This selective approach improves data accuracy while minimizing unnecessary processing time for already-valid data
Data Source
AI summary
The current subject matter relates to generation of relevant real-time offers based on global positioning system (GPS) data of an individual. A mobile device of an individual can record the GPS data of the individual. The mobile device can be connected to a central system. The central system can receive the recorded GPS data. The central system can predict, by using a trained predictive model and based on transaction history of the individual and the GPS data, categories of likely purchases by the individual. The central system can generate or reproduce offers from merchants of the predicted categories that are located within a threshold distance from a current location of the individual. The central system can send the generated offers to the mobile device that can display the generated offers in real-time. Other applications can include improving relevance of batch offers and/or real-time offers based on a recent purchase trigger.