Visited Location Labeling Using Contact Information
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Solution Overview
Problem
Existing location-tracking and awareness services face challenges in accurately associating geographic positions with relevant location labels, as prior art techniques often rely on incomplete or publicly listed data, degrading user experience.
Innovation Solution
A method for labeling locations visited by a user using a computer processor, involving collecting location history data, determining visited locations, and associating them with contact information from a user's contact list or receiving user-designated labels to provide contextually relevant labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If prior art techniques search only publicly listed locations to label geographic positions, then the system complexity remains low, but the location label relevance and data completeness deteriorate
Solution Approach 1:
The patent merges multiple data sources (publicly listed locations, contact information, calendar events, messaging app data) into a unified location labeling system. This combination allows the system to overcome the limitations of relying solely on public data by integrating personal contact information and communication data to generate more relevant and complete location labels.
Solution Approach 2:
The system uses an intermediary processing layer that analyzes communication data (emails, texts, calls) to extract location information and associate it with geographic positions. This intermediary layer bridges the gap between raw communication data and meaningful location labels, enabling the system to derive location relevance without directly accessing private contact databases.
2Measurement precision
If the system collects and processes comprehensive location history data and contact information, then the location label accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing contact information, calendar events, and communication data before they are needed for location labeling. Communication data is analyzed in advance to extract location patterns and associations, so when location labeling is required, the system can quickly retrieve pre-processed information rather than analyzing raw data from scratch.
Solution Approach 2:
The system applies different processing depths to different data sources based on their relevance and reliability. High-priority data sources (such as frequently contacted individuals or important calendar events) receive more intensive processing, while less critical data sources undergo lighter processing. This selective approach optimizes processing time while maintaining location label accuracy for the most relevant locations.
3Loss of information
If the system integrates multiple data sources including contact information and communication data, then the location awareness quality improves, but the data privacy and security requirements increase
Solution Approach 1:
The system employs an intermediary processing layer that handles sensitive communication data without exposing raw personal information. The intermediary analyzes emails, texts, and call logs to extract location patterns while maintaining data privacy through techniques such as anonymization, aggregation, and selective processing. This intermediary layer ensures that location awareness quality improves through comprehensive data analysis while mitigating privacy risks through controlled data access and processing.
Solution Approach 2:
The system transforms sensitive raw communication data into less sensitive derived parameters such as location patterns, frequency metrics, and association scores. By changing the parameter representation from personal identifiable information to aggregated statistical measures, the system maintains location awareness quality while reducing data privacy risks. The transformed parameters retain the necessary information for accurate location labeling but are less vulnerable to privacy breaches.
Data Source
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AI summary
Labeling Visited Locations Based on Contact Information The systems and techniques disclosed herein provide the ability to determine locations visited by a user and associate relevant location labels with the locations visited based on contact information. In some examples, a location label can be applied based on a match between a location visited and information stored in a user's contact list. In other examples, a user can efficiently designate a contact and location label to be associated with a location visited. In still other examples, if a location visited by a user is not listed in the user's contact list, but is otherwise known to the system, the location visited can be appropriately labeled and the corresponding contact in the user's contact list can be updated to include the location visited.