Contextual Location Labeling Using Device Activity Data
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Solution Overview
Problem
Existing map services fail to effectively label significant locations on a user's device, leading to a poor user experience as they often display generic or meaningless labels for frequently visited places, such as restaurants or homes, due to uncertainty in geographic location and lack of contextual data utilization.
Innovation Solution
A computing device determines a significant location by analyzing contextual data, including payment transactions, calendar events, reminders, map data, and communication records, to assign meaningful labels to locations based on user interactions and preferences, thereby personalizing the map experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If map services use generic location labeling, then implementation complexity is low, but user experience deteriorates due to meaningless labels for significant locations
Solution Approach 1:
The system performs preliminary actions by collecting contextual data (payment transactions, calendar events, reminders, communications) and analyzing movement patterns before the user needs location labels. This pre-processing enables the system to automatically identify significant locations and assign meaningful labels without requiring complex real-time processing when labels are needed.
Solution Approach 2:
The system uses existing contextual data that is already being collected by the computing device for other purposes (payment processing, calendar management, communication). By repurposing this existing data, the system avoids additional data collection overhead and enables automatic location labeling without requiring users to manually input information or the system to actively seek additional data.
2Measurement precision
If map services collect additional contextual data to improve location labeling, then labeling accuracy improves, but data collection and processing resources increase
Solution Approach 1:
The system achieves multi-functionality by using contextual data collected for multiple purposes: payment transaction data serves both financial recording and location identification; calendar events serve both scheduling and location labeling; movement patterns serve both navigation and significant location detection. This universal use of existing data eliminates the need for separate data collection systems.
Solution Approach 2:
The computing device already collects contextual data (payments, calendar events, communications, movement patterns) for its core functions. The map service leverages this self-collected data without requiring additional sensors, user inputs, or external data sources, thereby improving labeling accuracy without increasing data collection resources.
3Measurement precision
If map services analyze multiple contextual data sources, then significant location identification accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of contextual data sources continuously in the background, building profiles of significant locations and their associated labels before queries are made. By pre-processing and organizing data from multiple sources (payments, calendar, communications, movement), the system reduces the computational burden during actual location identification, thereby maintaining high accuracy without excessive processing delays.
Data Source
AI summary
Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.