Personalized Significant Location Labels from Contextual Data
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Solution Overview
Problem
Existing map services often display significant locations with generic labels that lack meaning to users, leading to a poor user experience, as they fail to utilize contextual data to personalize location labels.
Innovation Solution
A computing device determines a significant location based on contextual data, such as payment transactions, calendar events, map usage history, and communication data, to assign meaningful labels to locations that are important to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If map services use generic labels for significant locations, then the system complexity is reduced and data processing is simpler, but the user experience deteriorates due to lack of personalization and meaning
Solution Approach 1:
The system automatically analyzes existing contextual data (payment transactions, calendar events, map usage history, communication data) to generate personalized location labels without requiring user input or manual configuration. The computing device self-serves by autonomously determining significant locations and assigning meaningful labels based on inferred user importance, eliminating the need for complex user-facing configuration interfaces while delivering personalized results
Solution Approach 2:
The system uses multiple existing data sources (payment transactions, calendar events, map usage history, communication data) for a single purpose of label generation. By making these diverse data sources serve the universal function of personalizing location labels, the system avoids creating separate specialized systems for each data type while achieving comprehensive personalization
2Ease of operation
If map services collect additional contextual data for personalization, then the user experience is improved through meaningful labels, but the data collection and processing resources increase
Solution Approach 1:
The system combines multiple existing data sources (payment transactions, calendar events, map usage history, communication data) into a unified analysis process for label generation. By merging these data streams and processing them through a single contextual analysis mechanism, the system avoids creating separate processing pipelines for each data type, reducing overall computational complexity while leveraging the combined information value
Solution Approach 2:
The system performs preliminary analysis of contextual data to identify significant locations and their associated labels before the user needs them. By pre-processing and pre-identifying meaningful locations from the contextual data, the system reduces the computational burden during actual map usage, as the heavy lifting of data analysis is completed in advance
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.