Location History Analysis for Visit Intent Classification
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Solution Overview
Problem
Current location-based applications often send unwanted notifications to users who are merely passing by a location, leading to annoyance and disabling of potentially useful features due to false alarms and repetitive alerts.
Innovation Solution
The system analyzes location history data to differentiate between intended extended visits and brief pass-throughs by generating conditions based on signal detection and duration, ensuring relevant notifications are sent only for extended visits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If location-based applications send notifications whenever a user arrives at a location, then users receive relevant information about places they visit, but users who are merely passing by receive unwanted notifications leading to annoyance and disabling of features
Solution Approach 1:
The system performs preliminary analysis of location history data before sending notifications. It examines past visit patterns, duration, and frequency to pre-determine whether a user is likely to be making an extended visit or merely passing by. This preliminary classification allows the system to filter out pass-through locations before notifications are generated, preventing user annoyance while maintaining relevant information delivery.
Solution Approach 2:
The system uses feedback from historical location data to continuously improve notification accuracy. By analyzing past user behavior patterns including visit duration, frequency of returns, and time of day patterns, the system refines its understanding of intended versus incidental visits. This feedback loop enables the system to adapt to individual user patterns and improve the reliability of notification delivery over time.
2Reliability
If the system analyzes location history data to differentiate between extended visits and pass-throughs, then false alarms are minimized, but system complexity increases due to data processing requirements
Solution Approach 1:
The system performs self-service by utilizing already-collected location history data that users have been providing for other purposes. Rather than requiring additional sensors or complex new data collection mechanisms, the system repurposes existing location data to infer visit intent. This approach minimizes additional hardware or system complexity while achieving improved detection accuracy through intelligent data reanalysis.
Solution Approach 2:
The system changes parameters by focusing analysis on specific temporal characteristics of location data, such as visit duration thresholds and frequency patterns. By transforming raw location coordinates into temporal behavior patterns, the system achieves sophisticated visit classification using relatively simple analytical rules. This parameter transformation approach maintains reliability while controlling processing complexity.
Data Source
AI summary
Pieces of location history data corresponding to an identified place of interest are obtained. Each piece of the obtained location history data references one or more signals detected at the identified place of interest and also references temporal data that corresponds to the detected signal(s). Some of the obtained pieces of location history data that represent one or more short, pass-through visits at the identified place of interest are identified. Based on the identified pieces that represent the short visit(s), conditions for determining the intent of future detected location visits at the identified place of interest are generated. The generated conditions can then be employed to determine that a detected location visit of a particular computing device determined to be at the identified place of interest is an extended visit as opposed to a short visit.