Realtime Activity Suggestion via Aggregated Geo-Data
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Solution Overview
Problem
Existing applications lack the ability to recommend activities based on event listings and social check-in data, failing to aggregate activities into higher-level categories, display activity regions effectively on maps, combine multiple activities, and allow user-specified temporal browsing.
Innovation Solution
The architecture aggregates real-time geographically referenced data to suggest activities across spatial extents, using event listings and social data to assign scores for trending activities, while ensuring user privacy by sanitizing data and providing activity profiles for personalized suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system displays exact locations of user-contributed data, then the precision and detail of location information is improved, but user privacy is compromised
Solution Approach 1:
The patent extracts only the necessary aggregated location information (spatial extents with high activity) while removing personally identifiable location data. The system takes out the harmful element (exact user locations) while preserving the useful element (activity patterns) through aggregation and anonymization.
Solution Approach 2:
The patent merges multiple individual user location data points into aggregated spatial extents. By combining data from multiple users and representing it as generalized activity regions rather than individual locations, the system achieves both privacy protection and meaningful location-based recommendations.
2Measurement precision
If the system aggregates detailed user social data for personalized recommendations, then the accuracy of activity suggestions is improved, but user privacy and data security are worsened
Solution Approach 1:
The system extracts aggregate behavioral patterns from social data while removing personally identifiable information. It takes out the essential recommendation-driving insights (activity preferences, temporal patterns) while eliminating the privacy-risk-containing individual user data.
Solution Approach 2:
The patent applies different levels of data aggregation to different spatial and temporal scales. At the individual user level, data is fully anonymized; at the aggregate level, detailed patterns are preserved for recommendation accuracy. This local differentiation of data quality maintains privacy while enabling personalized suggestions.
3Adaptability or versatility
If the system provides comprehensive activity recommendations across multiple categories and time periods, then the versatility and usefulness of the service is improved, but the complexity of data aggregation and processing is worsened
Solution Approach 1:
The patent segments the recommendation system into modular components: spatial extent aggregation module, temporal pattern analysis module, activity categorization module, and recommendation generation module. Each segment handles a specific aspect of the data processing, making the overall complex system more manageable and maintainable while providing versatile recommendations.
Solution Approach 2:
The patent creates a universal data aggregation framework that handles multiple activity types, time periods, and spatial extents through a single unified system. The same core aggregation mechanisms serve multiple recommendation purposes, reducing overall system complexity despite the versatility of outcomes.
4Speed
If the system processes real-time social data for up-to-date recommendations, then the timeliness and relevance of suggestions is improved, but the computational resources and processing time are worsened
Solution Approach 1:
The patent implements periodic aggregation of social data at defined time intervals rather than continuous real-time processing. This periodic action provides timely recommendations while significantly reducing computational energy consumption by processing data in batches rather than continuously.
Solution Approach 2:
The system processes only the necessary portion of social data required for recommendation generation, focusing on aggregate patterns rather than individual user activities. This partial processing approach maintains recommendation timeliness while reducing overall computational burden and energy consumption.
Data Source
AI summary
Architecture that aggregates realtime geo-referenced data over areas such as physical world geographical areas and virtually-defined areas such as by geofences to provide users with a quick overview and suggestion of activities to do across an area of interest in the spatial extent. The geo-referenced data can be supplied by a provider and/or user. When in combination, event listings can be obtained from providers and social data (e.g., check-in) can be obtained from social websites and/or businesses that make check-in data available freely or under subscription, for example. At least one advantageous outcome of the disclosed aggregation approach is that privacy issues, which currently exist in the industry by showing exact locations of user-contributed data, are overcome. While aggregating over larger spatial extents having high activity, the events supplied by provider listings are assigned scores that show trending and/or high-user activity volumes, and therefore, can be suggested to users.